In the hypothetical case where zero bytes are transmitted, we can get the minimum possible latency on the system Equation 2.
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The value of is also known as the theoretical or zero-bytes latency. It is worth noticing that is not the only player in the equation, is called network bandwidth, the maximum transfer rate that can be achieved. There are different benchmarks used to measure communication latency. The action can be run multiple times using varying message lengths, timings are averaged to avoid measurement errors.
As described by the time formula at Equation 1 , different measures of transmission time are obtained depending on the package size. To get the minimum latency an empty package is used.
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These exercise the system from an application level, integrating all components performance for a common goal. It is worth mentioning that there are other methods that work at a lower level of abstraction, for instance using Netperf 9 or by following RFC 10 techniques. However these last two measure latency at network protocol and device level respectively.
HPC Cluster Optimization? Machine Learning to the Rescue!
High Performance Linpack is a portable benchmark for distributed-memory systems doing pure matrix multiplication It provides a testing and timing tool to quantify cluster performance. The HPC Challenge benchmark suite 12 packages 7 benchmarks:. It is expected that latency optimizations impact their results differently. Given a simplified system view of a cluster, there are multiple compute nodes that together run the application.
An application uses software such as libraries that interface with the operating system to reach hardware resources through device drivers. This work analyzes the following components:. Further work to optimize performance is always possible; only the most relevant optimizations were considered according to gathered experience over more than 5 years on the engineering of volume HPC solutions. As any other piece of software, device drivers implement algorithms which, depending on different factors, may introduce latency. Drivers may even expose hardware functionalities or configurations that could change the device latency to better support the Beowulf usage scenario.
Interrupt moderation is a technique to reduce CPU interrupts by caching them and servicing multiple ones at once Although it make sense for general purpose systems, this introduces extra latency, so Ethernet drivers should not moderate interruptions when running in HPC clusters. To turn off Interrupt Moderation on Intel network drivers add the following line on each node of the cluster and reload the network driver kernel module. Refer to documentation 14 for more details. For maintenance reasons some Linux distributions do not include the configuration capability detailed above.
In those cases, the following command can be used to get the same results.
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There is no portable approach to query kernel modules configurations in all Linux kernel versions, so configuration files should be used as a reference. Some system services may directly affect network latency.
Migrating IRQs to be served from one CPU to another is a time consuming task that although balance the load it may affect overall latency. The main objective of having such a service is to balance between power-savings and optimal performance. The task it performs is to dynamically distribute workload evenly across CPUs and their computing cores.
An ideal setup will assign all interrupts to the cores of a same CPU, also assigning storage and network interrupts to cores near the same cache domain.
Optimizing HPC Applications with Intel Cluster Tools : Hunting Petaflops - enanatulifyb.ml
However this implies processing and routing the interrupts before running them, which has the consequence of adding a short delay on their processing. Turning off the irqbalance service will help then to decrease network latency. In a Red Hat compatible system this can be done as follows:. As compute nodes are generally isolated on a private network reachable only through the head node, the firewall may not even be required. The system firewall needs to review each package received before continuing with the execution. This overhead increases the latency as incoming and outgoing packet fields are inspected during communication.
Linux-based systems have a firewall in its kernel that can be controlled throughout a user-space application called iptables. The Linux TCP stack implementation has different packet lists to handle incoming data, the PreQueue can be disabled so network packets will go directly into the Receive queue. In Red Hat compatible systems this can be done with the command:.
There are others parameters that can be analyzed 15 , but the impact they cause are too application specific to be included on a general optimization study. Using IMB Ping Pong as workload, the following results Figure 4 reflect how the different optimizations impact communication latency.
The actual figures on average and deviation are shown below at Table 3. The principal cause of overhead in communication latency is then IRQ moderation. Another important contributor is the packet firewall service. In the case of the IRQ balance service, the impact is only minimal. Optimizations impact vary, and not surprisingly they are not accumulative when combining them all.
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The problem size was customized to Ns NBs Ps Qs for a quick but still representative execution with a controlled deviation. As we can see on the results, the actual synchronization cycle done by the algorithm heavily relies on having low latency. The linear system is partitioned in smaller problem blocks which are distributed over a grid of processes which may be on different compute nodes.
The distribution of matrix pieces is done using a binary tree among compute nodes with several rolling phases between them. The performance figures differ across packaged benchmarks as they measure system characteristics that are affected by latency in diverse ways. However, the actual latency, bandwidth and PTRANS benchmark are impacted as expected due they communication dependency.
In order to double check if any of the optimization have hidden side effects and the real impact on the execution of a full-fledge HPC application, a real-world code was exercised. Table 6 shows the actual averaged figures after multiple runs. This shows that the results of a synthetic benchmark like IMB Ping Pong can not be used directly to extrapolate figures, they are virtually the limit to what can be achieved by an actual application. The experiments done as part of this work were done over 32 nodes with the following bill of materials Table 7. Figure 6 summarizes the complete optimization procedure.
It is basically a sequence of steps involving checking and reconfiguring Ethernet drivers and system services if required.
Optimizing Hpc Applications With Intel Cluster Tools
Enabling TCP extensions for low latency is not included due their negative consequences. The steps below include the purpose and an example of the actual command to execute as required on Red Hat compatible systems. Questions 1 helps to dimension the required work to optimize driver configuration to properly support network devices. Once gathered all the information required to known if optimizations can be applied, the following list can be used to apply configuration changes.
Between each change a complete cycle of measurement should be done. This include contrasting old and new latency average plus their deviation using at least IMB Ping Pong. This work shows that by only changing default configurations the latency of a Beowulf system can be easily optimized, directly affecting the execution time of High Performance Computing applications. As a quick reference, an out-of-the-box system using Gigabit Ethernet has around 50 s of communication latency. Using different techniques, it is possible to get as low as nearly 20 s.
This work also contrasted those methods and provided insights on how they should be executed and their results analyzed. Our experts would be happy to guide you if you have any questions! For dense High Performance Computing clusters, we provide up to processor cores in a standard 42U rackmount cabinet. Our NumberSmasher Twin 2 servers include four dual-processor compute nodes totaling cores in a 2U chassis: effectively doubling rack capacity.
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