High Performance Network Adapters and protocols

High performance network adapters are designed to provide fast and efficient data transfer between servers, storage systems, and other devices in a data center or high-performance computing environment. They typically offer advanced features such as high bandwidth, low latency, RDMA support, and offload capabilities for tasks such as encryption and compression. These adapters are often used in high-performance computing, cloud computing, and data center environments to support large-scale workloads and high-speed data transfer. Some examples of high-performance network adapters include:

• Mellanox ConnectX-6 and ConnectX-6 Dx
• Intel Ethernet Converged Network Adapter X710 and X722
• Broadcom BCM957810A1008G Network Adapter
• QLogic QL45212HLCU-CK Ethernet Adapter
• Solarflare XtremeScale X2522/X2541 Ethernet Adapter
• Chelsio T6 and T6E-CR Unified Wire Adapters

High-performance network adapters typically use specialized protocols that are designed to provide low-latency and high-bandwidth communication between systems. Some examples of these protocols include:

1. Remote Direct Memory Access (RDMA): A protocol that allows data to be transferred directly between the memory of one system and another, without involving the CPU of either system.
2. RoCE (RDMA over Converged Ethernet): An extension of RDMA that allows RDMA traffic to be carried over Ethernet networks.
3. iWARP: A protocol that provides RDMA capabilities over standard TCP/IP networks.
4. InfiniBand: A high-speed interconnect technology that provides extremely low-latency and high-bandwidth communication between systems.

These protocols are typically used in high-performance computing (HPC) environments, where low-latency and high-bandwidth communication is critical for achieving maximum performance. They are also used in other applications that require high-speed data transfer, such as machine learning, data analytics, and high-performance storage systems. Some examples of adapter features include:

• Advanced offloading capabilities: High-performance adapters can offload CPU-intensive tasks such as packet processing, encryption/decryption, and compression/decompression, freeing up server resources for other tasks.
• Low latency: Many high-performance adapters are designed to minimize latency, which is especially important for applications that require fast response times, such as high-frequency trading, real-time analytics, and scientific computing.
• Scalability: Some adapters support features such as RDMA and SR-IOV, which allow multiple virtual machines to share a single adapter while maintaining high performance and low latency.
• Security: Many high-performance adapters have hardware-based security features such as secure boot, secure firmware updates, and hardware-based encryption/decryption, which can help protect against attacks and data breaches.
• Management and monitoring: High-performance adapters often come with tools for monitoring and managing network traffic, analyzing performance, and troubleshooting issues.

A network adapter, also known as a network interface card (NIC), is a hardware component that allows a computer or other device to connect to a network. It typically includes a connector for a cable or antenna, as well as the necessary electronics to transmit and receive data over the network. Network adapters can be internal, installed inside the computer or device, or external, connected via USB or other ports. They are used for wired or wireless connections and support different types of networks such as Ethernet, WiFi, Bluetooth, and cellular networks.

source

A host bus adapter (HBA) is a hardware component that connects a server or other device to a storage area network (SAN). It is responsible for managing the flow of data between the server and the storage devices on the SAN. HBAs typically include a connector for a Fibre Channel or iSCSI cable, as well as the necessary electronics to transmit and receive data over the SAN. They are used to connect servers to storage devices such as disk arrays, tape libraries, and other storage systems.

Common Network Protocols Used in Distributed Storage:

1. IB: used for the front-end storage network in the DPC scenario.
2. RoCE: used for the back-end storage network.
3. TCP/IP: used for service network.

Network adapters are used to connect a computer or device to a network, while host bus adapters are used to connect a computer or device to a storage area network. Network adapters are used for data transmission over networks, while host bus adapters are used for data transmission over storage area networks. There are several network adapters that are commonly used in servers, and the best option will depend on the specific needs of the server and the network it will be connecting to. Some popular options include:

1. Intel Ethernet Converged Network Adapter X520-DA2: This is a 10 Gigabit Ethernet adapter that is designed for use in data center environments. It supports both copper and fiber connections and is known for its high performance and reliability.
2. Mellanox ConnectX-4 Lx EN: This is another 10 Gigabit Ethernet adapter that is designed for use in data centers. It supports both copper and fiber connections and is known for its low latency and high throughput.
3. Broadcom BCM57416 NetXtreme-E: This is a 25 Gigabit Ethernet adapter that is designed for use in data centers. It supports both copper and fiber connections and is known for its high performance and reliability.
4. Emulex LPe1605A: This is a 16 Gbps Fibre Channel host bus adapter (HBA) that is designed for use in storage area networks (SANs). It supports both N_Port ID Virtualization (NPIV) and N_Port Virtualization (NPV) and is known for its high performance and reliability.

IBM produces a wide range of servers for various types of environments, here are a few examples of IBM servers:

1. IBM Power Systems: These servers are designed for high-performance computing and big data workloads, and are based on the Power architecture. They support IBM's AIX, IBM i, and Linux operating systems.
2. IBM System x: These servers are designed for general-purpose computing and are based on the x86 architecture. They support a wide range of operating systems, including Windows and Linux.
3. IBM System z: These servers are designed for mainframe computing and support IBM's z/OS and z/VM operating systems.
4. IBM BladeCenter: These servers are designed for blade server environments and support a wide range of operating systems, including Windows and Linux.
5. IBM Storage: These servers are designed for storage and data management workloads, and support a wide range of storage protocols and operating systems.
6. IBM Cloud servers: IBM Cloud servers are designed for cloud-based computing and are based on the x86 architecture. They support a wide range of operating systems, including Windows and Linux.

Emulex Corporation Device e228 is a network adapter produced by Emulex Corporation. It is an Ethernet controller, which means it is responsible for controlling the flow of data packets over an Ethernet network. The Emulex Corporation Device e228 is part of the Emulex OneConnect family of network adapters, which are designed for use in data center environments. These adapters are known for their high performance, low latency, and high throughput. They also provide advanced features such as virtualization support, Quality of Service (QoS) and offloads (TCP/IP, iSCSI, and FCoE) to improve network performance. It supports 10Gbps Ethernet and can be used in both copper and fiber connections. This adapter is typically used in servers and storage systems that require high-speed network connections and advanced features to support data-intensive applications. The "be2net" kernel driver is a Linux device driver that is used to control the Emulex Corporation Device e228 network adapter. A kernel driver is a type of low-level software that interfaces with the underlying hardware of a device, such as a network adapter. It provides an interface between the hardware and the operating system, allowing the operating system to communicate with and control the device. The "be2net" driver is specifically designed to work with the Emulex Corporation Device e228 network adapter, and is responsible for managing the flow of data packets between the device and the operating system. It provides the necessary functionality for the operating system to access the adapter's features and capabilities, such as configuring network settings, monitoring link status and performance, and offloading network processing tasks. The be2net driver is typically included with the Linux operating system and it's loaded automatically when the device is detected. It's also available as a separate package, that can be installed and configured manually.

The Mellanox Technologies MT28800 Family [ConnectX-5 Ex Virtual Function] is a network adapter produced by Mellanox Technologies. It is an Ethernet controller, which means it is responsible for controlling the flow of data packets over an Ethernet network. This adapter is part of the Mellanox ConnectX-5 Ex family of network adapters, which are designed for use in data center environments. These adapters are known for their high performance, low latency, and high throughput. They support 100 Gbps Ethernet, RoCE v2 and InfiniBand protocols and provide advanced features such as virtualization support, Quality of Service (QoS), and offloads to improve network performance. It's worth noting that the Mellanox ConnectX-5 Ex Virtual Function is a specific type of adapter that is designed to be used in virtualized environments. It allows multiple virtual machines to share a single physical adapter, thus providing better flexibility and resource utilization. This adapter is typically used in servers, storage systems, and other high-performance computing devices that require high-speed network connections and advanced features to support data-intensive applications such as big data analytics, machine learning, and high-performance computing.

The Mellanox Technologies MT28800 Family [ConnectX-5 Ex Virtual Function] and the Emulex Corporation Device e228 are both network adapters, but there are some key differences between them: Speed and protocol support: The Mellanox ConnectX-5 Ex supports 100 Gbps Ethernet, RoCE v2 and InfiniBand protocols, while the Emulex Device e228 supports 10 Gbps Ethernet. This means that the Mellanox adapter is capable of higher data transfer speeds and can support multiple protocols for different types of networks.

Advanced features: Both adapters offer advanced features such as virtualization support, Quality of Service (QoS), and offloads. However, the Mellanox ConnectX-5 Ex also supports features like hardware-based time stamping, hardware-based packet filtering and dynamic rate scaling.

Target market: The Mellanox ConnectX-5 Ex is designed for use in data center environments, while the Emulex Device e228 is also designed for data center environments. Mellanox ConnectX-5 Ex is more geared towards high-performance computing and big data analytics, while the Emulex Device e228 is more geared towards general data center use.

Virtualization: Mellanox ConnectX-5 Ex Virtual Function is a specific type of adapter that is designed to be used in virtualized environments, allowing multiple virtual machines to share a single physical adapter, thus providing better flexibility and resource utilization. Emulex Device e228 supports virtualization, but it does not have a specific virtual function version. In summary, the Mellanox ConnectX-5 Ex is a high-speed, high-performance network adapter that offers advanced features and support for multiple protocols, while the Emulex Device e228 is a lower-speed, general-purpose network adapter that is geared towards data center environments.

Mellanox Technologies produces networking equipment, including network adapters. Some of the Mellanox adapters that support CNA (Converged Network Adapter) are:

1. Mellanox ConnectX-5 CNA: This adapter supports both Ethernet and Fibre Channel over Ethernet (FCoE) on a single adapter, and provides high-performance, low-latency data transfer.
2. Mellanox ConnectX-6 CNA: This adapter supports 100 GbE and 200 GbE speeds and provides hardware offloads for RoCE, iWARP and TCP/IP, in addition to supporting FC and FCoE protocols.
3. Mellanox ConnectX-5 EN CNA: This adapter supports both Ethernet and InfiniBand protocols, providing high-performance, low-latency data transfer for data center and high-performance computing environments.
4. Mellanox ConnectX-6 Lx CNA: This adapter supports 25 GbE and 50 GbE speeds, and provides hardware offloads for RoCE, iWARP, and TCP/IP, in addition to supporting FC and FCoE protocols.

Slingshot is a high-performance network fabric developed by the company Cray, now owned by Hewlett Packard Enterprise. It is designed to provide low-latency and high-bandwidth communication between nodes in high-performance computing systems, such as supercomputers and data centers. It is based on a packet-switched network architecture, with each node connected to a network switch. It supports a range of network topologies, including fat-tree, hypercube, and dragonfly. The fabric is designed to be scalable, with support for thousands of nodes. It uses a range of advanced features to optimize performance, including adaptive routing, congestion control, and quality-of-service (QoS) mechanisms. It also includes support for features such as remote direct memory access (RDMA) and messaging passing interface (MPI) offload, which can further improve application performance. Overall, Slingshot is designed to provide high-performance, low-latency communication for demanding HPC workloads, making it a popular choice for large-scale scientific simulations, data analytics, and other compute-intensive applications.

RDMA Types As discussed before , there are three types of RDMA networks: Infiniband, RDMA over Converged Ethernet (RoCE), and iWARP.

source

The InfiniBand network is specially designed for RDMA to ensure reliable transmission at the hardware level. The technology is advanced, but the cost is high. RoCE and iWARP are both Ethernet-based RDMA technologies, which enable RDMA with high speed, ultra-low latency, and extremely low CPU usage to be deployed on the most widely used Ethernet.

source

The three RDMA networks have the following characteristics:

1. InfiniBand: RDMA is considered at the beginning of the design to ensure reliable transmission at the hardware level and provide higher bandwidth and lower latency. However, the cost is high because IB NICs and switches must be supported.
2. RoCE: RDMA based on Ethernet consumes less resources than iWARP and supports more features than iWARP. You can use common Ethernet switches that support RoCE NICs.
3. iWARP: TCP-based RDMA network, which uses TCP to achieve reliable transmission. Compared with RoCE, on a large-scale network, a large number of TCP connections of iWARP occupy a large number of memory resources. Therefore, iWARP has higher requirements on system specifications than RoCE. You can use common Ethernet switches that support iWARP NICs.

Comparison between RoCE and InfiniBand

Infiniband is a high-performance, low-latency interconnect technology used to connect servers, storage, and other data center equipment. It uses a switched fabric topology and supports both data and storage traffic. InfiniBand adapters are specialized network interface cards (NICs) that are designed to work with InfiniBand networks. Here are a few examples of InfiniBand adapters:

Mellanox ConnectX-4/5: These adapters support both 40 Gb/s and 100 Gb/s InfiniBand and provide high-performance, low-latency data transfer for data center and high-performance computing environments.

Mellanox ConnectX-6: This adapter supports 200 Gb/s InfiniBand, providing hardware offloads for RoCE, iWARP and TCP/IP, in addition to supporting FC and FCoE protocols.

Intel Omni-Path Architecture (OPA) 100 Series: This adapter supports 100 Gb/s InfiniBand and provides high-performance, low-latency data transfer for data center and high-performance computing environments.

Qlogic InfiniPath HTX: This adapter supports 10 Gb/s InfiniBand and provides high-performance, low-latency data transfer for data center and high-performance computing environments.

Mellanox ConnectX-4 Lx: This adapter supports 25 Gb/s and 50 Gb/s InfiniBand and provides hardware offloads for RoCE, iWARP, and TCP/IP, in addition to supporting FC and FCoE protocols.

RoCE (RDMA over Converged Ethernet) is a network protocol that allows for low-latency, high-throughput data transfer over Ethernet networks. It leverages Remote Direct Memory Access (RDMA) capabilities to accelerate communications between applications hosted on clusters of servers and storage arrays. It is based on the Remote Direct Memory Access (RDMA) protocol, which allows for direct memory access over a network without involving the CPU, resulting in low-latency and high-bandwidth data transfer. RoCE uses standard Ethernet networks and devices, so it is simpler to set up and manage than traditional RDMA over Infiniband. RoCE is designed for use in data center environments, and is particularly well-suited for use with high-performance computing and big data analytics applications, which require high-speed, low-latency data transfer. Some features of RoCE are:

• Low-latency: RoCE allows for very low-latency data transfer, which is critical for high-performance computing and big data analytics applications.
• High-throughput: RoCE allows for high-bandwidth data transfer, which is necessary for handling large amounts of data.
• RDMA support: RoCE is based on the RDMA protocol, which allows for direct memory access over a network, resulting in low-latency and high-bandwidth data transfer.
• Converged Ethernet: RoCE uses standard Ethernet networks and devices, making it simpler to set up and manage than traditional RDMA over Infiniband.
• Quality of Service (QoS) support: RoCE can provide Quality of Service (QoS) feature, which allows for guaranteed bandwidth and low-latency for critical applications.
• Virtualization support: RoCE can be used with virtualized environments, allowing multiple virtual machines to share a single physical adapter, thus providing better flexibility and resource utilization.

RoCE Overview RDMA over Converged Ethernet (RoCE) is a network protocol that leverages Remote Direct Memory Access (RDMA) capabilities to accelerate communications between applications hosted on clusters of servers and storage arrays. RoCE incorporates the IBTA RDMA semantics to allow devices to perform direct memory-to-memory transfers at the application level without involving the host CPU. Both the transport processing and the memory translation and placement are performed by the hardware which enables lower latency, higher throughput, and better performance compared to software-based protocols.

Infiniband RDMA to RoCE :

Both InfiniBand RDMA and RoCE can implement remote memory access network protocols. The two currently have their own advantages and disadvantages on the market, and both are used in HPC cluster architecture or large-scale data centers. Comparing the two, InfiniBand has better performance. But InfiniBand is a dedicated network technology. It cannot inherit the user's accumulation and platform of operation on the IP network, which causes the high cost in operation and maintenance. Therefore, carrying RDMA based on the traditional Ethernet network is also inevitable for the large-scale application of RDMA. To guarantee RDMA performance and network layer communication, many network switches use RoCEv2 to carry high-performance distributed applications.

CNA (Converged Network Adapter) is a type of network adapter that supports multiple protocols, such as Ethernet and Fibre Channel over Ethernet (FCoE), on a single adapter. A CNA typically includes both a NIC and a Host Bus Adapter (HBA) to support both data and storage traffic. When using a CNA with SRIOV (Single Root I/O Virtualization) and ROCE (RDMA over Converged Ethernet), multiple virtual functions (VFs) can be created on the CNA, each with its own MAC address, VLAN ID, and other network attributes. Each VF can be assigned to a different virtual machine (VM) or a container, and each VM or container can have its own network configuration and parameters. Each VF can be configured to support ROCE, allowing for low-latency, high-throughput data transfer over Ethernet networks. This can be particularly useful in high-performance computing and big data analytics environments, where low-latency and high-bandwidth data transfer is critical.

converged network adapter (CNA)

SRIOV with ROCE on a CNA can provide the following benefits: Improved resource utilization: By allowing multiple VMs or containers to share a single physical adapter, SRIOV with ROCE on a CNA can improve resource utilization and reduce costs.

Improved network performance: ROCE allows for low-latency, high-throughput data transfer over Ethernet networks, which can improve network performance in high-performance computing and big data analytics environments.

Fine-grained control of network resources: SRIOV with ROCE on a CNA allows for fine-grained control of network resources, allowing each VM or container to have its own network configuration.

I/O Virtualization Overview: CNA, SR-IOV

Differences between RoCE, Infiniband RDMA, and TCP/IP.

The fastest network adapter available today depends on the specific application and the network infrastructure. Generally, there are different types of network adapters that support different speeds and protocols, and each one is suitable for different use cases.

For example, for data center environments, 100 GbE (gigabit ethernet) adapters are currently considered as the fastest option, providing high-bandwidth and low-latency data transfer. These adapters use the latest technologies such as SFP28 and QSFP28 connectors and support both copper and fiber cabling. Mellanox ConnectX-6, Intel Omnipath and Marvell FastLinQ are some examples of 100 GbE adapters.

For High-Performance Computing (HPC) and Artificial Intelligence (AI) applications, Infiniband adapters are considered as the fastest option providing low-latency and high-bandwidth data transfer. Mellanox ConnectX-6 HDR and Intel OPA 100 series are examples of 200 Gb/s Infiniband adapters. For storage, Fibre Channel (FC) and Fibre Channel over Ethernet (FCoE) adapters are considered as the fastest option providing low-latency and high-bandwidth data transfer. Mellanox ConnectX-6, Emulex Gen 6 Fibre Channel and Qlogic Gen 6 Fibre Channel are examples of these adapters.

Supercomputer systems, like the Summit and Sierra, developed by Oak Ridge National Laboratory and Lawrence Livermore National Laboratory, respectively, use a high-performance interconnect technology called Infiniband for their internal communication. Mellanox Technologies is the company that provides the Infiniband adapters and host bus adapters (HBAs) for these supercomputers.

Summit and Sierra use Mellanox's Connect-IB adapter which supports 100 Gb/s InfiniBand and provides hardware offloads for RoCE, iWARP and TCP/IP. The Connect-IB adapters are designed to handle the high-bandwidth and low-latency requirements of large-scale supercomputing applications. The Host Bus Adapter (HBA) Mellanox ConnectX-4 Lx is used for these systems. ConnectX-4 Lx is a single-port 100 Gb/s InfiniBand adapter that supports both 25 Gb/s and 50 Gb/s speeds. The adapters provide hardware offloads for RoCE, iWARP, and TCP/IP, in addition to supporting FC and FCoE protocols.

Frontier is a planned supercomputer that is being developed by Oak Ridge National Laboratory and Cray Inc. It is the world's fastest supercomputer in 2021. According to the information available, Frontier uses high-performance interconnect technology called Slingshot, developed by Cray, for its internal communication. Slingshot is a next-generation interconnect technology that promises to provide low-latency, high-bandwidth, and high-message-rate data transfer.

In terms of network adapters and host bus adapters (HBAs), the information available is not specific, but it's known that Cray has collaborated with Mellanox Technologies to provide the network interconnect technology for Frontier. This suggests that Mellanox's InfiniBand and/or Slingshot adapters may be used in Frontier.

A host bus adapter (HBA) is a specialized type of network interface card (NIC) that connects a host computer to a storage area network (SAN). HBAs provide a bridge between the host computer and the storage devices, allowing the host to access and manage the storage devices as if they were locally attached.

Here are a few key things to know to get familiar with storage HBAs:

• Protocols: HBAs support different storage protocols such as Fibre Channel (FC), Fibre Channel over Ethernet (FCoE), and iSCSI. FC and FCoE are commonly used in enterprise environments, while iSCSI is more commonly used in smaller, SMB environments. Speed: HBAs are available in different speeds, such as 8 Gb/s, 16 Gb/s, and 32 Gb/s. Higher speeds provide faster data transfer and improved performance.
• Multi-Path Support: HBAs often support multi-path I/O, which allows multiple paths to the storage devices to be used for failover and load balancing. Compatibility: HBAs are designed to work with specific operating systems, so it's important to check the compatibility of the HBA with the operating system you are using.
• Management and Monitoring: Many HBAs include management and monitoring software that allows administrators to view and configure the HBA's settings, such as Fibre Channel zoning, and to monitor the performance of the HBA and the storage devices it is connected to. Driver and Firmware: HBA's require driver and firmware to work properly, so it's important to ensure that the HBA has the latest driver and firmware updates installed.
• Vendor Support: It's important to consider the vendor support of the HBA, as well as the warranty and technical support options available, as these can be critical factors when choosing an HBA. Architecture: Some HBAs are based on ASIC (Application-Specific Integrated Circuit) while others on FPGA (Field-Programmable Gate Array) architecture, both have their own pros and cons.

Power10 is the latest generation of IBM's Power Architecture designed for high-performance computing and big data workloads, and is intended to deliver significant performance and efficiency improvements over its predecessor, Power9. Some of the key features of the Power10 architecture include:

• Higher core count: Power10 processors have a higher core count than Power9 processors, which allows for more parallel processing and improved performance.
• Improved memory bandwidth: Power10 processors have more memory bandwidth than Power9 processors, which allows for faster data transfer between the processor and memory. Enhanced security features: Power10 processors include enhanced security features, such as hardware-enforced memory encryption and real-time threat detection, to protect against cyber-attacks.
• Improved energy efficiency: Power10 processors are designed to be more energy efficient than Power9 processors, which can help to reduce power consumption and cooling costs. Optimized for AI workloads: Power10 processors are optimized for AI workloads and have better support for deep learning and other AI-related tasks.
• More flexible and open: Power10 architecture is more flexible and open. It supports more operating systems, and it has more open interfaces and more standard protocols to connect to other devices. Example: IBM Power Systems AC922.

AI workloads refer to tasks that involve the use of artificial intelligence and machine learning algorithms, such as:

• Natural Language Processing (NLP): This includes tasks such as speech recognition, text-to-speech, and machine translation.
• Computer Vision: This includes tasks such as image recognition, object detection, and facial recognition. Predictive analytics: This includes tasks such as forecasting, anomaly detection, and fraud detection.
• Robotics: This includes tasks such as navigation, object manipulation, and decision making. Recommender Systems: This includes tasks such as personalized product recommendations, content recommendations, and sentiment analysis.
• Generative Models: These include tasks such as image and video generation, text generation and music generation. Reinforcement learning: These include tasks such as game playing, decision making and control systems.
• Deep Learning: These include tasks such as Image and speech recognition, natural language processing and predictive analytics.

These are just a few examples of AI workloads, there are many more possible applications of AI in various industries such as healthcare, finance, transportation, and manufacturing. As AI technology continues to advance, new possibilities for AI workloads will continue to emerge.

OpenMPI and UCX are both middleware for high-performance computing, but they are not directly connected to adapter design. However, they can utilize hardware-specific features and optimizations of network adapters to improve performance.

MPI (Message Passing Interface) and AI (Artificial Intelligence) are interrelated because MPI can be used to distribute the computational workload of AI applications across multiple nodes in a distributed computing environment. Many AI algorithms, such as deep learning, machine learning, and neural networks, require a significant amount of computational resources, memory, and data storage. These algorithms can be parallelized and run in a distributed environment using MPI, which allows them to take advantage of the computing power of multiple nodes. MPI can be used to distribute the data and the workload of AI applications across multiple nodes, enabling parallel processing and reducing the time required to complete the computation. This can significantly improve the performance of AI applications and enable researchers to train and optimize more complex models. Moreover, MPI can be integrated with other libraries, such as OpenMP, CUDA, and UCX, to further improve the performance of AI applications. For example, CUDA is a parallel computing platform that enables programmers to use GPUs (Graphics Processing Units) for general-purpose processing, and MPI can be used to distribute the workload across multiple GPUs and nodes. In summary, MPI provides a scalable and efficient way to distribute the computational workload of AI applications across multiple nodes, enabling researchers and developers to build and run more complex models and achieve better performance. The choice of MPI communication method that is best suited for AI workloads depends on the specific characteristics of the workload and the system architecture. However, some general guidelines can help in selecting the appropriate MPI communication method for AI workloads. For AI workloads that involve large amounts of data, non-blocking point-to-point communication and collective communication methods are generally preferred. Non-blocking point-to-point communication methods, such as MPI_Isend and MPI_Irecv, allow the application to continue processing while the communication is in progress, which can help reduce the overall communication time. Collective communication methods, such as MPI_Allreduce and MPI_Allgather, can also be highly effective in AI workloads, as they enable efficient data sharing and synchronization among multiple nodes. These methods can be used to distribute the workload of an AI application across multiple nodes, enabling parallel processing and reducing the time required to complete the computation. Additionally, the choice of MPI communication method may also depend on the underlying system architecture. For example, on a system with a high-speed interconnect, such as InfiniBand, the use of MPI communication methods that take advantage of the RDMA (Remote Direct Memory Access) capabilities of the interconnect, such as UCX, can provide significant performance benefits. The best MPI communication method for AI workloads depends on the specific characteristics of the workload and the system architecture. However, non-blocking point-to-point communication and collective communication methods are generally preferred for AI workloads that involve large amounts of data, and the use of RDMA-enabled MPI communication methods can provide significant performance benefits on high-speed interconnects.

The mapping of adapters in supercomputers and network adapters is an important aspect of designing and building a supercomputer. In general, supercomputers use high-performance network adapters that can handle large amounts of data at high speeds, with low latency and high bandwidth. The choice of network adapter depends on the specific requirements of the supercomputer, such as the type and size of data being processed, the number of nodes in the system, and the desired performance characteristics. Some of the common network adapters used in supercomputers include InfiniBand adapters, Ethernet adapters, and Omni-Path adapters. These adapters are typically integrated with the server hardware, either as separate network interface cards (NICs) or as part of the motherboard design. These adapters provide low-latency, high-bandwidth interconnects between nodes in a cluster, enabling parallel computing and large-scale data processing. In addition to high-performance interconnects, HPC also relies on specialized hardware accelerators like GPUs, FPGAs, and ASICs to offload compute-intensive tasks from the CPU and improve overall system performance. These accelerators are often used in combination with high-performance network adapters to enable faster data transfer and processing in HPC environments.

IBM offloaded Watson Health assets to investment firm Francisco Partners

IBM Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson. IBM’s then- CEO Ginni Rometty called the project a “moon shot,” but her replacement was less enthused about the business. The computer system was initially developed to answer questions on the quiz show Jeopardy!.

IBM launched Watson Health in early 2015 and made a series of acquisitions that cost $4 billion. They included Merge Healthcare, Truven Health Analytics, Phytel, and Explorys. IBM sold Watson Health for $1B, which is 25% of what it paid to acquire four strong businesses. The assets involved include Health Insights, MarketScan, Clinical Development, Social Program Management, Micromedex, and imaging software products. IBM offloaded Watson Health this year because it doesn't have the requisite vertical expertise in the healthcare sector.

Talking at stock market analyst Bernstein's 38th Annual Strategic Decisions Conference, the big boss was asked to outline the context for selling the healthcare data and analytics assets of the business to private equity provider Francisco Partners for $1 billion in January.

 The Watson brand will be  carrier for AI.

It's a question of verticals versus horizontals. IBM believes that they are best positioned to take these technologies.They will always have an industry lens but through their consulting team. They want to work on technologies that are horizontal across all industries."

Verticals should belong to people who really have all of the domain expertise, they have credibility in that vertical. And healthcare companies, people in medical devices, they will have the credibility to carry out how AI is applied to health in depth i.e AI as applied to healthcare, to financial services, to compliance, in that case, regulatory compliance, is going to be a massive market.

To succeed in health, they need doctors and nurse practitioners to speak to the buyers of Watson Health. That's not the IBM go-to-market field force, so there's a misalignment. Ditto in Promontory, that needed  ex-regulators and accountants to go talk to people worrying about financial compliance. So, that's a little bit different than IBM. IBM still sells Watson solutions in financial services, advertising, business automation, and video streaming and hosting. As for AI in the enterprise where  inflation, labor costs and the world undergoing a "demographic shift" means that "there are fewer people with the skills" and so AI and automation will be "applied to more and more domains." Trend is going to reverse in the next few decades."

IBM’s Watson Health is being sold for parts. The technology giant has agreed to sell the division’s data and analytics assets to private equity firm Francisco Partners. Terms weren’t disclosed, although Bloomberg values the deal at more than $1 billion. Launched in 2015, Watson Health’s goal was to revolutionize medicine through AI. After years of pricey expansion — it spent more than $4 billion on acquisitions, per Axios — and reports of ineffectiveness, the unit scaled back its ambitions.

Once viewed as a flagship of AI in medcine and life science, IBM Watson Health couldn't live up to its ambitious promises to transform everything from drug discovery to cancer care. It would be interesting to see how the new firm who bought this giant from IBM will transform its data and analytic assets and realize their full potential.

BIG MPI

In order to describe a structured region of memory, the routines in the MPI standard use a (count, datatype) pair. The C specification for this convention uses an int type for the count. Since C int types are nearly always 32 bits large and signed, counting more than 2 power 31 elements poses a challenge. Instead of changing the existing MPI routines, and all consumers of those routines, the MPI Forum asserts that users can build up large datatypes from smaller types. To evaluate this hypothesis and to provide a user-friendly solution to the large-count issue, we have developed BigMPI, a library on top of MPI that maps large count MPI-like functions to MPI-3 standard features. BigMPI demonstrates a way to perform such a construction, reveals shortcomings of the MPI standard, and uncovers bugs in MPI implementations

References:

https://www.mcs.anl.gov/papers/P5210-1014.pdf

https://github.com/jeffhammond/BigMPI

MPI [ Message Passing Interfaces] - behind the scenes

Parallel computing is accomplished by splitting up large and complex tasks across multiple processors. In order to organize and orchestrate parallel processing, our program must consider automatically decomposing the problem at hand and allowing the processors to communicate with each other when necessary while performing their work. This introduces a new overhead, the synchronization and the communication itself.

Computing parallelism can be roughly classified as Distributed Memory (DM) or Shared Memory(SM) class. In Distributed Memory (DM), each processor has its own memory which are connected through a network that can exchange data, thus, limiting the DM performance and scalability. In Shared

Memory (SM), each processor can access all of the memory, resulting in automatic distribution of procedurally iterations over several processors - autoparallelization, explicit distribution of work over the processors by compiler directives or function calls to threading libraries. If this overhead is not accounted. It can create several issues like bottlenecks in the parallel computer design and load imbalances.

MPI is an API for message passing between entities with separated memory spaces - processes. The standard doesn't care where those processes run - it could be on networked computers (clusters), it could be on a big shared memory machine or it could be any other architecture that provide the same semantics (e.g. IBM Blue Gene)

OpenMPI is a widely used message passing interface (MPI) library for parallel computing. It provides an abstraction layer that allows application developers to write parallel code without worrying about the underlying hardware details. However, OpenMPI also provides support for hardware-specific optimizations, including those for network adapters. For example, it supports the use of high-speed interconnects such as InfiniBand and RoCE, and it can take advantage of hardware offload capabilities such as Remote Direct Memory Access (RDMA).

UCX (Unified Communication X) is another middleware library for communication in distributed systems. It is designed to be highly scalable and to support a wide range of hardware platforms, including network adapters. UCX provides a portable API that allows applications to take advantage of hardware-specific features of network adapters, such as RDMA and network offloading. UCX can also integrate with other system-level libraries such as OpenMPI and hwloc to optimize performance on specific hardware configurations.

Hwloc (Hardware Locality) is a library for topology discovery and affinity management in parallel computing. It provides a portable API for discovering the hierarchical structure of the underlying hardware, including network adapters, and it allows applications to optimize performance by binding threads and processes to specific hardware resources. Hwloc can be used in conjunction with OpenMPI and UCX to optimize communication and data movement on high-performance computing systems.

TCP/IP is a family of networking protocols. IP is the lower-level protocol that's responsible for getting packets of data from place to place across the Internet. TCP sits on top of IP and adds virtual circuit/connection semantics. With IP alone you can only send and receive independent packets of data that are not organized into a stream or connection. It's possible to use virtually any physical transport mechanism to move IP packets around. For local networks it's usually Ethernet, but you can use anything. There's even an RFC specifying a way to send IP packets by carrier pigeon.

Sockets is a semi-standard API for accessing the networking features of the operating system. Your program can call various functions named socket, bind, listen, connect, etc., to send/receive data, connect to other computers, and listen for connections from other computers. You can theoretically use any family of networking protocols through the sockets API--the protocol family is a parameter that you pass in--but these days you pretty much always specify TCP/IP. (The other option that's in common use is local Unix sockets.)

When you are interested  to  write a parallel programming application, you should probably not be looking at TCP/IP or sockets as those things are going to be much lower level than you want. You'll probably want to look at something like MPI or any of the PGAS languages like UPC, Co-array Fortran, Global Arrays, Chapel, etc. They're going to be far easier to use than essentially writing your own networking layer.

When you use one of these higher level libraries,  you get lots of nice abstractions like collective operations, remote memory access, and other features that make it easier to just write your parallel code instead of dealing with all of the OS stuff underneath. It also makes your code portable between different machines/architectures.

MPI is free to use any available communication path(s) for MPI messages in the new communicator; the socket is only used for the initial handshaking. 

A common problem is the one of two processes each opening connections to each other. The socket code assume that the sockets are bidirectional, thus only one socket is needed by each pair of connected processes, not one socket for each member of the pair. Then it should refactor the states and state machine into a clear set of VC connection states and connection states.

There are three related objects used during a connection event. They are the connection itself (a structure specific to the communication method, sockets in the case of this note), the virtual connection, and the process group to which the virtual connection belongs

If a socket connection between two processes is established, there are always two sides: The connecting side and the accepting side. The connecting side sends an active message to the accepting side. This sides first accepts the connection. However, if both processes try to connect to each other (head-to-head situation), the processes n have both, a connecting and an accepting connection. In this situation, one of the connections is refused/discarded - while the other connection is established. This is decided on the accepting side.

State machines for establishing a connection:

Connect side :

The connecting side tries to establish a connection by sending an active message to the remote side. If the connection is accepted, the pg_id is send to the remote side. Now, the process waits, until the connection is finally accepted or refused. For this decision, the remote side requires the gp_id . Based on the answer from the remote side (ack = yes or ack = no) the connection is connected or closed.

Accept side:

The accept side receives a connection request on the listening socket. In the first instance, it accepts the connection an allocates the required structures (conn, socket). Then, the connection waits for the pg_id of the remote side to assign the socket-connection to a VC. The decision, if a connection is accepted or refused, is based on the following steps:

1. The VC has to aktive connection (vc->conn == NULL) : The new connection is accepted
2. The VC has an aktive connection
3. If my_pg_id < remote_pg_id: accept and discard other connection
4. If my_pg_id > remote_pg_id: refuse  

The answer is send to the remote note.

Data Types Required by the MPI Standard:

MPI point-to-point communication sends messages between two different MPI processes. One process performs a send operation while the other performs a matching read

MPI collectives: MPI provides a set of routines for communication patterns that involve all the processes of a certain communicator, so-called collectives. The two main advantages of using collectives are:

1) Less programming effort. 

2) Performance optimization, as the implementations are usually efficient, especially if optimized for specific architectures

For collective communication, significant gains in performance can be achieved by implementing topology- and performance-aware collectives.

Three common blocking collectives are Barrier(), Bcast() and Reduce().

1. Allreduce(). Combination of reduction and a broadcast so that the output is available for all processes.
2. Scatter(). Split a block of data available in a root process and send different fragments to each process.
3. Gather(). Send data from different processes and aggregate it in a root process.
4. Allgather(). Similar to Gather() but the output is aggregated in buffers of all the processes.
5. Alltoall(). All processes scatter data to all processes.

Reference:

https://www.sciencedirect.com/topics/computer-science/point-to-point-communication

https://wiki.mpich.org/mpich/index.php/Establishing_Socket_Connections

https://aist-itri.github.io/gridmpi/publications/cluster04-slide.pdf

High performance computing

 High-Performance Computing (HPC or supercomputer) is omnipresent in today’s society. For example, every time you watch Netflix, the recommendation algorithm leverages HPC resources remotely to offer you personalized suggestions. HPC stands for High-Performance Computing. The ability to carry out large scale computations to solve complex problems, that either need to process a lot of data, or to have a lot of computing power at their disposal. Basically, any computing system that doesn’t fit on a desk can be described as HPC. 

HPC systems are actually networks of processors. The key principle of HPC lies in the possibility to run massively parallel code to benefit from a large acceleration in runtime.  A common HPC capability is around 100,000 cores. Most HPC applications are complex tasks which require the processors to exchange their results. Therefore, HPC systems need very fast memories and a low-latency, high-bandwidth communication systems (>100Gb/s) between the processors as well as between the processors and the associated memories.

We can differentiate two types of HPC systems: the homogeneous machines and the hybrid ones. Homogeneous machines only have CPUs while the hybrids have both GPUs and CPUs. Tasks are mostly run on GPUs while CPUs oversee the computation. 

They have more computing power since GPUs can handle millions of threads simultaneously and are also more energy efficient. GPUs have faster memories, require less data transfer and are capable to exchange with other GPUs, which is the most energy-intensive part of the machine.

source

High Performance Computing used to be strictly defined with high speed network to allow strong interconnections between cores. The rise of AI applications led to an architecture based on more independent clusters but still massively parallel.

HPC systems also include the software stack. That can be divided into three categories. First the user environment encompasses the applications known as workflows. Then the middleware linking applications and their implementation on the hardware. It includes the runtimes and frameworks. Last, the Operating system, at system level with the job scheduler, management software for load balancing and data availability. Its role is to assign tasks to the processors and organize the exchange of data between the processors and the memories to ensure the best performance.

HPC applications

HPC provides many benefits and value when used for commercial and industrial applications. Applications that can be classified in five categories:

- Fundamental research aims to improve scientific theories to better understand natural or other phenomena. HPC enables more advanced simulations leading to breakthrough discoveries.

- Design simulation allows industries to digitally improve the design of their products and test their properties. It enables companies to limit prototyping and testing, making the designing process quicker and less expensive.

- Behavior prediction enables companies to predict the behavior of a quantity which they can’t impact but depend on, such as the weather or the stock market trends. HPC simulations are more accurate and can look farther into the future thanks to their superior computing abilities. It is especially important for predictive maintenance and weather forecasts.

- Optimization is a major HPC use case. It can be found in most professional fields, from portfolio optimization to process optimization, to most manufacturing challenges faced by the industry.

HPC is more and more used for data analysis. Business models, industrial processes and companies are being built on the ability to connect, analyze and leverage data, making supercomputers a necessity in analyzing massive amounts of data.

The 5 fields of HPC Applications.

Another major application for HPC is in the fields of medical and material advancements. For instance, HPC can be deployed to:

Combat cancer: Machine learning algorithms will help supply medical researchers with a comprehensive view of the U.S. cancer population at a granular level of detail.

Identify next-generation materials: Deep learning could help scientists identify materials for better batteries, more resilient building materials and more efficient semiconductors.

Understand patterns of disease: Using a mix of artificial intelligence (AI) techniques, researchers will identify patterns in the function, cooperation, and evolution of human proteins and cellular systems.

HPC needs are skyrocketing. A lot of sectors are beginning to understand the economic advantage that HPC represents and therefore are developing HPC applications. 

Industrial companies in the field of aerospace, automotive, energy or defence are working on developing digital twins of a machine or a prototype to test certain properties. This requires a lot of data and computing power in order to accurately represent the behavior of the real machine. This will, moving forward, render prototypes and physical testing less and less standard.

The HPC dynamics and industrial landscape

source

The limits of a model :

Unfortunately, supercomputers are revealing some limits. First of all, some problems are not currently solvable by a supercomputer. The race to the exascale (a supercomputer able to realize 10^18 floating point operations per second) is not necessarily going to solve this issue. Some problems or simulations might remain unsolvable, or at least, unsolvable in an acceptable length of time. For example, in the case of digital twins or molecular simulation, calculations have to be greatly simplified in order for current computers to be able to make them in an acceptable length of time (for product or drug design).

Moreover, a second very important challenge is the power consumption. The consumption of computing and data centers represents 1% of power consumption in the world and this is bound to significantly increase. It shows that this model is unsustainable in the long term, especially since exascale supercomputers will most surely consume more than current ones. Not only is it technically unsustainable, it is also financially so. Indeed, a supercomputer can cost as much as 10mUSD per year in electricity consumption.

The new chips revolution

CPUs and GPUs are not the only solutions to tackle the two previously stated issues.

Although most efforts are focused on developing higher-performance CPU and GPU-powered supercomputers in order to reach the exascale, new technologies, in particular “beyond Silicon”, are emerging. Innovative chip technologies could act as accelerators like GPUs did in the 2010s and significantly increase the computing power. Moreover, some technologies, such as quantum processors for example, would be able to solve new categories of problems that are currently beyond our reach.

In addition, 70% of the energy consumption in a HPC is accounted for by the processors. Creating new chips, more powerful and more energy efficient would enable us to solve both problems at once. GPUs were the first step towards this goal. Indeed, for some applications, GPUs can replace up to 200 or 300 CPUs. Although one GPU individually consumes a bit more than a CPU (400W against 300W approximately), overall, a hybrid supercomputer will consume less than a homogeneous supercomputer of equal performance.

The model needs to be reinvented to include disruptive technologies. Homogeneous supercomputers should disappear, and it is already underway. In 2016, only 10 out of the supercomputers in the Top500 were hybrid. By 2020, within only four years, it rose to 333 out of 500, including 6 in the top 10.

At Quantonation, are convinced that innovative chips integrated in hetereogeneous supercomputing architectures, as well as optimized softwares and workflows, will be key enablers to face societal challenges by significantly increasing sustainability and computing power. We trust that these teams are ready to face the challenge and be part of the future of compute:

1. Pasqal’s neutral atoms quantum computer, highly scalable and energy efficient;
2. Lighton’s Optical Processing Unit, a special purpose ligh-based AI chip fitted for tasks such as Natural Language Processing;
3. ORCA Computing’s fiber based photonic systems for simulation and fault tolerant quantum computing;
4. Quandela’s photonic qubit sources that will fuel next generation of photonic devices;
5. QuBit Pharmaceuticals software suites leveraging HPC and quantum computing resources to accelerate drug discovery ;
6. Multiverse Computing’s solutions using disruptive mathematics to resolve finance’s most complex problems on a range of classical and quantum technologies.

IBM Cloud HPC IaaS for building HPC environments using IBM’s Virtual Private Cloud (VPC). It enables you to create your own configuration for Compute Instances; High-performance Storage and Networking like Public Gateways, Load Balancers and Routers. Multiple connectivity options are available upto 80Gbps and IBM Cloud offers the highest level of security and encryption with FIPS 140-2 Level 4. Also available is IBM Code Engine, a fully managed serverless platform to run containers, applications or batch jobs.

– Spectrum Computing provides intelligent dynamic hybrid cloud capabilities which enables organizations to use cloud resources according to defned policies. Spectrum LSF and Symphony allows you to burst workloads to the cloud, dynamically provision cloud resources and intelligently move data to manage egress costs. It also enables the ability for auto scaling to take full advantage of consumption-based pricing and pay for cloud resources only when they are needed.

– Spectrum Scale is an enterprise grade High Performance File System (HPFS) that delivers scalable capacity and performance to handle demanding data analytics, content repositories and HPC workloads. Spectrum Scale architecture allows it to handle tens of thousands of clients, billions of fles and petabytes of data written and retrieved as fles or objects with low latency. Optionally, IBM Aspera can be used for high speed data movement using the FASP protocol.

Use Cases

– Financial Services: Monte Carlo simulation, risk modeling, actuarial sciences

– Health and Life Sciences: Genome analysis, drug discovery, bio-sequencing, clinical treatments, molecular modeling

– Automotive: Vehicle drag coeffcient analysis, crash simulation, engine combustion analysis, air flow modeling

– Aerospace: Structural, fluid dynamics, thermal, electromagnetic and turbine flow analysis

– Electronic Design Automation (EDA): Integrated Circuit (IC) and Printed Circuit Board (PCB) design and analysis

– Oil and Gas: Subsurface terrain modeling, reservoir simulation, seismic analysis

– Transportation: Routing logistics, supply chain optimization

– Energy & Utility: Severe storm prediction, climate, weather and wind modelling

– Education/Research: High energy physics, computational chemistry 

The reign and modern challenges of the Message Passing Interface (MPI)

All good, but why do you guys doing numerical linear algebra and parallel computing always use the Message Passing Interface to communicate between the processors?”

MPI begun about 25 years ago and has been since then, undoubtedly, the “King” of HPC. What were the characteristics of MPI that made it the de-facto language of HPC?

MPI-3 has added several interfaces to enable more powerful communication scheduling, for example nonblocking collective operations and neighborhood collective operations. 

Much of the big data community moved from single-nodes to parallel and distributed computing to process larger amounts of data using relatively short-lived programs and scripts. Thus, programmer productivity only played a minor role in MPI/HPC while it was one of the major requirements for big data analytics. While MPI codes are often orders-of-magnitude faster than many big data codes, they also take much longer to develop. And that is most often a good trade-off.

MPI I/O has been introduced nearly two decades ago to improve the handling of large datasets in parallel settings. It is successfully used in many large applications and I/O libraries such as HDF-5. 

MPI predates the time when the use of accelerators became commonplace. However, when these accelerators are used in distributed-memory settings such as computer clusters, then MPI is the common way to program them. The current model, often called MPI+X (e.g., MPI+CUDA), combines traditional MPI with accelerator programming models (e.g., CUDA, OpenACC, OpenMP etc.) in a simple way. In this model, MPI communication is performed by the CPU. Yet, this can be inefficient and inconvenient and recently, we have proposed a programming model called distributed CUDA (dCUDA) to perform communication from within a CUDA compute kernel [3]. This allows to use the powerful GPU warp scheduler for communication latency hiding. In general, integrating accelerators and communication functions is an interesting research topic.

Programming at the transport layer, where every exchange of data has to be implemented with lovingly hand-crafted sends and receives or gets and puts, is an incredibly awkward fit for numerical application developers, who want to think in terms of distributed arrays, data frames, trees, or hash tables. 

Everyone uses MPI” has made it nearly impossible for even made-in-HPC-land tools like Chapel or UPC to make any headway, much less quite different systems like Spark or Flink, meaning that HPC users are largely stuck with using an API which was a big improvement over anything else available 25 years ago, 

Chapel is a modern programming language designed for productive parallel computing at scale. Chapel's design and implementation have been undertaken with portability in mind, permitting Chapel to run on multicore desktops and laptops, commodity clusters, and the cloud, in addition to the high-end supercomputers for which it was originally undertaken.

Reference:

https://medium.com/quantonation/a-beginners-guide-to-high-performance-computing-ae70246a7af

https://github.com/ljdursi/mpi-tutorial/blob/master/presentation/presentation.md

https://github.com/chapel-lang/chapel

https://blog.xrds.acm.org/2017/02/message-passing-interface-mpi-reign-modern-challenges/

================

Test log Analytics with Elasticsearch and Kibana

Software Test Analytics is the process of collecting and analyzing data from software testing activities to improve the quality and efficiency of the testing process. This can include metrics such as test coverage, defect density, and test execution time, as well as data on test automation and test case management. The goal of software test analytics is to identify trends and patterns in the data that can be used to make informed decisions about how to improve the testing process, such as where to focus testing efforts, which tests to automate, and how to optimize test case design.

Test log analysis is the process of collecting, analyzing, and interpreting data from test logs in order to identify patterns, trends, and issues that can help improve the quality and performance of software systems. This can include data such as test results, error messages, performance metrics, and other relevant information. The goal of test log analysis is to help identify and resolve issues that may be impacting the performance or functionality of the system, and to improve the overall quality of the software. Common techniques used in test log analysis include statistical analysis, machine learning, and data visualization.

Insights that can be gained from analyzing test case output logs are listed below: 

1) Identifying which test cases are passing and which are failing, and the reasons for the failures. This can help you to focus your testing efforts on the areas of the application that need the most attention.

2) Understanding the performance of the application under test. This can include metrics such as response time, memory usage, and CPU usage, which can help you to identify and fix performance bottlenecks.

3) Identifying patterns in the test case data that indicate potential issues with the application under test. For example, if a large number of test cases are failing in a particular module, it could indicate a problem with that module that needs to be investigated.

4) Identifying areas of the application that are not being adequately tested. This can help you to create new test cases to cover these areas, and to ensure that the application is thoroughly tested before release.

5) Identifying where automation can improve the testing process. With the help of logs, you can automate test cases which are repetitive, time-consuming or prone to human errors.

6) To get the most value out of test case output logs, it's important to have a systematic and automated way of collecting and analyzing the data. This can include using tools such as log analyzers, data visualization tools, and automated reporting tools.

Open-source frameworks that can be used for test result analytics: 

ELK Stack: ELK stands for Elasticsearch, Logstash, and Kibana. Elasticsearch is a search engine, Logstash is a log aggregator, and Kibana is a data visualization tool. By using the ELK stack, you can collect, store, and analyze large volumes of test result data in real-time.

The ELK stack (Elasticsearch, Logstash, and Kibana) can be integrated with a CI (Continuous Integration) framework in several ways to show Test analytics.

1. Logstash: Logstash can be used to collect and parse log files generated by the CI framework. You can configure Logstash to read log files from the CI server and to parse the data into a format that can be indexed by Elasticsearch.

2. Kibana: Kibana can be used to visualize the data collected by Logstash. You can create a dashboard in Kibana that displays metrics such as build time, test execution time, and pass/fail rate.

3. Elasticsearch: Elasticsearch can be used to store and index the data collected by Logstash. You can use Elasticsearch to search and analyze the data, and to create complex queries and visualizations.

4. Integrate with CI/CD tool: You can integrate ELK stack with your CI/CD tool, for example, Jenkins, Travis or CircleCI. You can configure the CI tool to send log files to Logstash, or directly to Elasticsearch.

-------------------

After Installation and configuring the ELK stack and Jenkins, the next step for analytics on test reports logs would be to start analyzing and visualizing the data. Here are some steps you can take:

1. Verify data collection: Ensure that the data is being collected correctly and that the logs are being indexed in Elasticsearch.

2. Create visualizations: Use Kibana to create visualizations such as line charts, bar charts, and pie charts to represent the data in a meaningful way. These visualizations can be used to represent metrics such as build time, test execution time, and pass/fail rate.

3. Create dashboards: Use Kibana to create dashboards that display multiple visualizations in a single view. These dashboards can be used to monitor the build and test results in real-time and to analyze the data over time.

4. Define alerts: Set up alerts in Kibana to notify you when certain conditions are met, such as a high number of test failures.

5. Analyze the data: Use Elasticsearch to create complex queries and to analyze the data in more detail. This can be used to identify patterns and trends in the data that can help improve the testing process.

6. Improve test coverage: Use the data to identify areas of the application that are not being adequately tested and to focus your testing efforts on those areas.

7. Identify and fix defects: Use the data to identify the root cause of test failures and to fix defects in the application

------------------------

We can ingest data with Python on Elastic search instead of using logstash which is good for realtime data ingestion. Prerequisites: Get elasticsearch packages

python -m pip install elasticsearch
python -m pip install elasticsearch-async

Some commonly used elasticsearch APIs listed below :

1. es.index: Used to index a document into an index.
2. es.search: Used to search for documents in an index based on a query.
3. es.get: Used to retrieve a document from an index by its ID.
4. es.delete: Used to delete a document from an index by its ID.
5. es.update: Used to update a document in an index.
6. es.count: Used to count the number of documents that match a query without returning the actual documents.
7. es.exists: Used to check if a document exists in an index.
8. es.bulk: Used to execute multiple index, update, or delete requests in a single HTTP request.
9. es.create: Used to create a new index.
10. es.delete_index: Used to delete an existing index.
11. es.get_mapping: Used to retrieve the mapping of a document type in an index.
12. es.put_mapping: Used to define the mapping of a document type in an index.
13. es.cluster.health: Used to retrieve information about the health of the Elasticsearch cluster.
14. es.cluster.state: Used to retrieve the current state of the Elasticsearch cluster.
15. es.nodes.info: Used to retrieve information about the nodes in the Elasticsearch cluster.
16. es.nodes.stats: Used to retrieve statistics about the nodes in the Elasticsearch cluster.
17. es.termvectors: Used to retrieve information about the terms in a document.
18. es.mtermvectors: Used to retrieve information about the terms in multiple documents.
19. es.explain: Used to explain how a particular document matches a query.
20. es.mget: Used to retrieve multiple documents from an index by their IDs

Example : Python code to use the Elasticsearch Python client to index a test log report into an Elasticsearch index:

Pre-requisite: Elasticsearch Python client installed (pip install elasticsearch)

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

# cat my_test_dataToES.py

#Let me create a sample test report data and push it to ElasticsearchDB

from datetime import datetime
from elasticsearch import Elasticsearch
# create an Elasticsearch client instance
es = Elasticsearch("http://sachin.bengaluru.com:9200")

# create an Elasticsearch index to store the test log report
es.indices.create(index='test_logs_sachin', ignore=400)

# define the test log report as a Python dictionary
test_log = {
    'test_name': 'login_test',
    'status': 'failed',
    'error_message': 'Invalid credentials',
    'timestamp': datetime.now()
}
# index the test log report into the Elasticsearch index
es.index(index='test_logs_sachin', body=test_log)
#

Execute the python script 

# python3 my_test_dataToES.py

#

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Check the kibana dashboard and create ==> Create index pattern

Visualizing testing results can help you to quickly and easily identify patterns and trends in the data, and can provide valuable insights into the performance and quality 

1) Test Execution Progress: Creating a graph or chart that shows the progress of test execution over time can help you to identify trends in test case pass/fail rates, and to identify areas of the application that are not being adequately tested.

2) Test Case Pass/Fail Rates: Creating a graph or chart that shows the pass/fail rate for each test case can help you to quickly identify which test cases are passing and which are failing. This can help you to focus your testing efforts on the areas of the application that need the most attention.

3) Defect Density: Creating a graph or chart that shows the number of defects per unit of code can help you to identify areas of the application that are prone to defects and to identify patterns in the types of defects that are being found.

4) Test Execution Time: Creating a graph or chart that shows the execution time for each test case can help you to identify performance bottlenecks and to optimize test case design.

5) Test Automation: Creating a graph or chart that shows the percentage of test cases that are automated can help you to identify areas of the application that can benefit from test automation.

6) Test coverage : Creating a graph or chart that shows how much of the application is being tested by your test suite can help you identify the areas that are not being covered and focus on increasing test coverage.

-----------------------------------------------

NOTE: 

Arrays: https://www.elastic.co/guide/en/elasticsearch/reference/current/array.html

In Elasticsearch, there is no dedicated array data type. Any field can contain zero or more values by default, however, all values in the array must be of the same data type. For instance:

an array of strings: [ "one", "two" ]

an array of integers: [ 1, 2 ]

an array of arrays: [ 1, [ 2, 3 ]] which is the equivalent of [ 1, 2, 3 ]

an array of objects: [ { "name": "Mary", "age": 12 }, { "name": "John", "age": 10 }]

Arrays of objects: Arrays of objects do not work as you would expect: you cannot query each object independently of the other objects in the array. If you need to be able to do this then you should use the nested data type instead of the object data type. This is explained in more detail in Nested.

Detailed insights from test result logs :

1) Root Cause Analysis: By analyzing the logs of failed test cases, you can identify the root cause of the failure, such as an issue with the application under test, a problem with the test case design, or an environment issue. This can help you to quickly fix the problem and prevent similar issues in the future.

2) Correlation Analysis: By analyzing the logs of multiple test cases, you can identify patterns and correlations between test results, such as the relationship between test execution time and the number of defects found. This can help you to identify areas of the application that are prone to defects and to optimize test case design.

3) Regression Analysis: By analyzing the logs of test cases that have been executed over time, you can identify trends in test case pass/fail rates, and to identify areas of the application that are not being adequately tested. This can help you to focus your testing efforts on the areas of the application that need the most attention.

4) Log Parsing: By parsing the logs, you can extract relevant information such as test case name, status, execution time, error messages, and stack trace. This information can be further analyzed to identify trends and patterns that can help improve the testing process.

5) Anomaly Detection: By analyzing the logs, you can identify anomalies or unexpected behavior in the test results. This can help you to identify potential issues with the application under test and to quickly fix them before they become major problems.

Machine Learning: You can use machine learning techniques such as clustering, classification, or prediction to analyze test results logs. This can help you to identify patterns and insights that would be difficult to discover manually.

Natural Language Processing: By using NLP techniques, you can extract useful information from unstructured test result logs. This information can be used to identify patterns and insights that would be difficult to discover manually.

These techniques can be implemented using machine learning libraries and frameworks such as scikit-learn, TensorFlow, or PyTorch.  It's also important to have a good understanding of the data, cleaning and preprocessing it before training the model.