Molecular Dynamics Simulations with Dell EMC PowerEdge XE8545 Server and NVIDIA A100
Wed, 02 Jun 2021 19:37:48 -0000
|Read Time: 0 minutes
Overview
Over the past decade, graphics processing units, or GPUs, have become popular in scientific computing because of their great ability to exploit a high degree of parallelism. NVIDIA has a handful of life sciences applications optimized and run on their general-purpose GPUs. Unfortunately, these GPUs can only be programmed with CUDA, OpenACC, and the OpenCL framework. Most members of the life sciences community are not familiar with these frameworks, and so few biologists or bioinformaticians can make efficient use of GPU architectures. However, GPUs have been making inroads into the molecular dynamics simulation (MDS) field since MD was developed in the 1950s. MDS requires heavy computational work to simulate biomolecular structures or their interactions.
In this blog, we tested two MDS applications; NAMD, and LAMMPS using the Dell EMC PowerEdge XE8545 server with NVIDIA A100 GPUs. Since the XE8545 server does not support NVIDIA V100 GPU, we can roughly estimate the performance boost with the A100 from our previous tests.
These two applications are free and open-source parallel MD packages designed for analyzing the physical movements of atoms and molecules.
The test server configuration is summarized in the following table.
Dell EMC PowerEdge XE8545 | |
CPU | 2x 7713 (Milan), 64 Cores, 2.0 GHz – 3.7 GHz Base-Boost, TDP 225 W, 256 MB L3 Cache |
RAM | DDR4 1024 GB (32 x 32 GB) 3200 MT/s |
Operating system | RHEL 8.3 (4.18.0-240.el8.x86_64) |
Filesystem network | Mellanox InfiniBand HDR100 |
Filesystem | Dell EMC Ready Solutions for HPC BeeGFS High Capacity Storage |
BIOS system profile | Performance Optimized |
Logical processor | Disabled |
Virtualization technology | Disabled |
Accelerator | 4 x A100-40 GB SXM4 |
Cuda/Toolkit | 11.2 |
OpenMPI | 4.1.1 |
NAMD | NAMD_Git-2021-04-01_Source |
LAMMPS | Stable version (29 Oct 2020) |
Performance Evaluation
NAMD
Nanoscale Molecular Dynamics (NAMD) is open-source software for molecular dynamics simulation written in a CHARMM parallel programming model and is designed for high-performance simulation of large biomolecular systems.
NAMD was built with the NAMD_Git-2021-04-01_Source source code on GCC 11.1 and CUDA 11.2. For our tests, we used two sets of data; 1.06 million-atoms of the Satellite Tobacco Mosaic Virus (STMV) system, and the HECBioSim3000k-atom system, which is a pair of 1IVO and 1NQL hEGFR tetramers.
Figure 1 shows the performance of 4x A100 GPUs with the STMV dataset. NAMD uses ++p options to specify the number of worker threads, and as recommended, is equal to the total number of cores minus the total number of GPUs. However, the number of total cores in the Milan Eypc 7003 family of processors, such as the Eypc 7713 that is used in the testing system, does not follow the generic recommendation. It seems to be around 79 to 90 cores. The optimal number of cores depends on the data size. Close to 9-nanosecond simulations (ns) per day performance is a significant performance gain from the NVIDIA V100 tests that we ran previously. It is difficult to say the performance gain is the sole contribution of the new A100 GPUs because the comparison of the 16 GB V100 on the Intel Skylake platform to the 40 GB A100 on the AMD Milan platform may not be valid.
Figure 1. Estimated simulation time per day with 4x NVIDIA A100 GPUs
The purpose of an additional test with 3 million atom protein tetramers is to confirm that the STMV test results are not artificial due to the relatively small icosahedron structure of SMTV, and the partial simulation of assembly and disassembly processes. Figure 2 shows the nanosecond simulations per day plot for 3000k-atom data. 2.1 ns/day seems to be close to the maximum performance with 64 cores.
Figure 2. Estimated simulation time per day with 4x NVIDIA A100 GPUs
LAMMPS
Large-scale Atomic/Molecular Massively Parallel Simulator, or LAMMPS, is a classical molecular dynamics code and has potentials for solid-state materials (metals and semiconductors), soft matter (biomolecules and polymers), and coarse-grained or mesoscopic systems. LAMMPS can model atoms, or can be used as a parallel particle simulator at the atomic, meso, or continuum scale. LAMMPS runs on single processors, or in parallel using message-passing techniques and spatial decomposition of the simulation domain. LAMMPS was built with GCC 11.1, OpenMPI 4.1.1, and CUDA 11.2 from the source. The 465k-atom system was selected from HECBioSim.
As shown in Figure 3, LAMMPS scales well over the number of A100s. With 4x A100 GPUs, a 8.4 ns/day simulation is achievable.
Figure 3. Estimated simulation time per day with various number of BPUs
Conclusion
Although it is not possible to compare the performance of the A100 and the V100 from this study, the Milan CPUs and A100 show a strong synergy between more cores with better and faster GPUs. Running NAMD and LAMMPS on the XE8545 with the A100 can deliver a better performance than a system with the V100.
Related Blog Posts
Nanoscale Molecular Dynamics (NAMD) Performance with Dell EMC PowerEdge R750xa & NVIDIA A series GPUs
Thu, 22 Jul 2021 09:03:25 -0000
|Read Time: 0 minutes
Overview
Over the past decade, GPUs have become popular in scientific computing because of their great ability to exploit a high degree of parallelism. NVIDIA has optimized life sciences applications to run on their general-purpose GPUs. Unfortunately, these GPUs can only be programmed with CUDA, OpenACC, or the OpenCL framework. Most of the life sciences community is not familiar with these frameworks so few biologists or bioinformaticians can make efficient use of GPU architectures. However, GPUs have been making inroads into the molecular dynamics simulation (MDS) field since MD was developed in the 1950s. MDS requires heavy computational work to simulate biomolecular structures or their interactions.
In this blog, the performance of one popular MDS application, NAMD, is presented with various NVIDIA A-series GPUs such as the A100, the A10, the A30 and the A40 . NAMD is a free and open-source parallel MD package designed for analyzing the physical movements of atoms and molecules.
Dell Technologies has released the new PowerEdge R750xa server, a GPU workload platform that is designed to support artificial intelligence, machine learning, and high-performance computing solutions. The dual socket/2U platform supports 3rd Gen Intel Xeon Scalable Processors (code named Ice Lake). It supports up to 40 cores per processor, has eight memory channels per CPU, and up to 32 DDR4 DIMMs at 3200 MT/s DIMM speed. This server can accommodate up to four double-width PCIe GPUs that are located in the front left and the front right of the server. The test server configurations are summarized in Table 1, and the specifications of tested NVIDIA GPUs are listed in Table 2.
Table 1: Tested compute node configuration
Test Beds | ||||
Server | Dell EMC PowerEdge R750xa | Dell EMC PowerEdge R740 | ||
CPU | Intel(R) Xeon(R) Platinum 8380 CPU @ 2.30 GHz | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40 GHz | Intel(R) Xeon(R) Gold 6248 CPU @ 2.50 GHz | |
NVIDIA GPUs | 4 x A100 | 4 x A10 | 4 x A30 | 2 x A40 |
RAM | DDR4 1024 GB (32 x 32 GB) 3200 MT/s | DDR4 384 GB (24 x 16 GB) 2933 MT/s | ||
Operating system | RHEL 8.3 (4.18.0-240.el8.x86_64) | |||
Filesystem network | Mellanox InfiniBand HDR100 | |||
Filesystem | Dell EMC Ready Solutions for HPC BeeGFS High Capacity Storage | |||
BIOS system profile | Performance Optimized | |||
Logical processor | Disabled | |||
Virtualization technology | Disabled | |||
Cuda/Toolkit | 11.2 | |||
OpenMPI | 4.1.1 | |||
NAMD | NAMD_Git-2021-04-01_Source |
Table 2: Specifications of tested NVIDIA GPUs
NVIDIA GPUs | ||||
| ||||
FP64 (TFLOPS) | 9.7 | Unknown | 5.2 | Unknown |
FP64 Tensor Core (TFLOPS) | 19.5 | Unknown | 10.3 | Unknown |
FP32 (TFLOPS) | 19.5 | 31.2 | 10.3 | 37.4 |
Tensor Float 32 (TFLOPS) | 156 | 312* | 62.5 | 125* | 82 | 165 * | 74.8 | 149.6* |
BFLOAT16 Tensor Core (TFLOPS) | 312 | 624* | 125 | 250* | 165 | 330* | 149.7 | 299.4* |
FP16 Tensor Core (TFLOPS) | 312 | 624* | 125 | 250* | 165 | 330* | 149.7 | 299.4* |
INT8 Tensor Core (TOPS) | 624 | 1248* | 250 | 500* | 330 | 661* | 299.3 | 598.6* |
INT4 Tensor Core (TOPS) | Unknown | 500 | 1,000* | 661 | 1321* | 598.7 | 1,197.4* |
GPU memory | 40 GB HBM2 | 24 GB GDDR6 | 24 GB HBM2 | 48 GB GDDR6 |
GPU memory bandwidth | 1,555 GB/s | 600 GB/s | 933 GB/s | 696 GB/s |
Max Thermal Design Power (TDP) | 400W | 150W | 165W | 300W |
Multi-Instance GPU | Up to 7 MIGs @ 5 GB | Unknown | 4 GPU instances @ 6 GB each 2 GPU instances @ 12 GB each 1 GPU instance @ 24 GB | Unknown |
Form factor | PCIe | Single-slot, full-height, full-length (FHFL) | Dual-slot, full-height, full-length (FHFL) | 4.4" (H) x 10.5" (L) dual slot |
Interconnect | PCIe Gen4: 64 GB/s | PCIe Gen4: 64 GB/s | PCIe Gen4: 64 GB/s
| PCIE Gen4 x 16 31.5 GB/s (bidirectional) |
* With sparsity
Performance Evaluation
NAMD
NAMD was compiled from source code (NAMD_Git-2021-04-01_Source) using GCC 11.1 and CUDA 11.2. We used a test data set, the 1.06 million-atom system of Satellite Tabacco Mosaic Virus (SMTV).
Figure 1 shows the performance of four GPUs with the STMV dataset. The figures represent the performance changes in nanoseconds per day (ns/day) with various numbers of cores used with one, two or four GPUs. The only valid comparison between the various GPUs is NVIDIA A100 and A10 since the test systems were configured identically. Although the performance of NAMD is affected by the CPU clock speed, the tested systems are not significantly different from the CPU’s clock speed. The A10 is rated at three times the single precision FLOPS of the A30, and the A10 performs better than the A30 on the two GPU tests even with slightly slower CPUs. The A100 outperformed by roughly 25 percent and 16 percent on single and two GPU tests when comparing the A10’s results, respectively.
The results from four GPU tests in Figure 1 show similar performance for the different GPUs. This agrees well with our previous test results that NAMD does not scale after two GPUs. We can rule out a potential argument that the data size might be too small since 3 million atom data, HECBioSim3000k-atom system, which is a pair of 1IVO and 1NQL hEGFR tetramers, shows similar or worse results (those results are not shown here).
Figure 1: NAMD performance with STMV, 1 million-atom system |
As shown in Figure 1, when four GPUs were tested , all of the GPUs except the A40 reached ~9 ns/day simulations. And, in terms of maximum performance, the A10 performs the highest number of simulations, 9.121 ns/day. However, these numbers are not true reflections of the performance due to the scalability limitations. Although all four GPU test results are similar, the A100 has a better throughput than other GPUs for the two GPU test as shown in Figure 2. Also, it is worth noting that the A10 and the A40 are not suitable for general-purpose computing due to the lack of double-precision support.
Figure 2 shows the performance comparisons among the different GPUs we tested in this study. Again, the A30 performed better than the A10 up to the 16 cores. It is difficult to determine why the A30 doesn’t perform as well with a large number of active CPU cores(20 and more).
Figure 2: STMV test results comparisons with two GPUs |
Conclusion
The A100 shows a dominant performance and is the most capable card among the A-series GPUs. Although the A30 did not perform as well as the A10 in our test , it is another outstanding choice for versatile applications.
The A10 performed well compared to the A30, and it is the successor of the T4, which was the most cost-effective solution for specific applications such as genomics data analysis.
Since it is not possible to obtain the accurate performance differences among A-series GPUs from this study, further investigation is necessary to achieve a clear picture of these general purpose GPUs.
Dell PowerEdge Servers Unleash Another Round of Excellent Results with MLPerf™ v4.0 Inference
Wed, 27 Mar 2024 15:12:53 -0000
|Read Time: 0 minutes
Today marks the unveiling of MLPerf v4.0 Inference results, which have emerged as an industry benchmark for AI systems. These benchmarks are responsible for assessing the system-level performance consisting of state-of-the-art hardware and software stacks. The benchmarking suite contains image classification, object detection, natural language processing, speech recognition, recommenders, medical image segmentation, LLM 6B and LLM 70B question answering, and text to image benchmarks that aim to replicate different deployment scenarios such as the data center and edge.
Dell Technologies is a founding member of MLCommons™ and has been actively making submissions since the inception of the Inference and Training benchmarks. See our MLPerf™ Inference v2.1 with NVIDIA GPU-Based Benchmarks on Dell PowerEdge Servers white paper that introduces the MLCommons Inference benchmark.
Our performance results are outstanding, serving as a clear indicator of our resolve to deliver outstanding system performance. These improvements enable higher system performance when it is most needed, for example, for demanding generative AI (GenAI) workloads.
What is new with Inference 4.0?
Inference 4.0 and Dell’s submission include the following:
- Newly introduced Llama 2 question answering and text to image stable diffusion benchmarks, and submission across different Dell PowerEdge XE platforms.
- Improved GPT-J (225 percent improvement) and DLRM-DCNv2 (100 percent improvement) performance. Improved throughput performance of the GPTJ and DLRM-DCNv2 workload means faster natural language processing tasks like summarization and faster relevant recommendations that allow a boost to revenue respectively.
- First-time submission of server results with the recently released PowerEdge R7615 and PowerEdge XR8620t servers with NVIDIA accelerators.
- Besides accelerator-based results, Intel-based CPU-only results.
- Results for PowerEdge servers with Qualcomm accelerators.
- Power results showing high performance/watt scores for the submissions.
- Virtualized results on Dell servers with Broadcom.
Overview of results
Dell Technologies delivered 187 data center, 28 data center power, 42 edge, and 24 edge power results. Some of the more impressive results were generated by our:
- Dell PowerEdge XE9680, XE9640, XE8640, and servers with NVIDIA H100 Tensor Core GPUs
- Dell PowerEdge R7515, R750xa, and R760xa servers with NVIDIA L40S and A100 Tensor Core GPUs
- Dell PowerEdge XR7620 and XR8620t servers with NVIDIA L4 Tensor Core GPUs
- Dell PowerEdge R760 server with Intel Emerald Rapids CPUs
- Dell PowerEdge R760 with Qualcomm QAIC100 Ultra accelerators
NVIDIA-based results include the following GPUs:
- Eight-way NVIDIA H100 GPU (SXM)
- Four-way NVIDIA H100 GPU (SXM)
- Four-way NVIDIA A100 GPU (PCIe)
- Four-way NVIDIA L40S GPU (PCIe)
- NVIDIA L4 GPU
These accelerators were benchmarked on different servers such as PowerEdge XE9680, XE8640, XE9640, R760xa, XR7620, and XR8620t servers across data center and edge suites.
Dell contributed to about 1/4th of the closed data center and edge submissions. The large number of result choices offers end users an opportunity to make data-driven purchase decisions and set performance and data center design expectations.
Interesting Dell data points
The most interesting data points include:
- Performance results across different benchmarks are excellent and show that Dell servers meet the increasing need to serve different workload types.
- Among 20 submitters, Dell Technologies was one of the few companies that covered all benchmarks in the closed division for data center suites.
- The PowerEdge XE8640 and PowerEdge XE9640 servers compared to other four-way systems procured winning titles across all the benchmarks including the newly launched stable diffusion and Llama 2 benchmark.
- The PowerEdge XE9680 server compared to other eight-way systems procured several winning titles for benchmarks such as ResNet Server, 3D-Unet, BERT-99, and BERT-99.9 Server.
- The PowerEdge XE9680 server delivers the highest performance/watt compared to other submitters with 8-way NVIDIA H100 GPUs for ResNet Server, GPTJ Server, and Llama 2 Offline
- The Dell XR8620t server for edge benchmarks with NVIDIA L4 GPUs outperformed other submissions.
- The PowerEdge R750xa server with NVIDIA A100 PCIe GPUs outperformed other submissions on the ResNet, RetinaNet, 3D-Unet, RNN-T, BERT 99.9, and BERT 99 benchmarks.
- The PowerEdge R760xa server with NVIDIA L40S GPUs outperformed other submissions on the ResNet Server, RetinaNet Server, RetinaNet Offline, 3D-UNet 99, RNN-T, BERT-99, BERT-99.9, DLRM-v2-99, DLRM-v2-99.9, GPTJ-99, GPTJ-99.9, Stable Diffusion XL Server, and Stable Diffusion XL Offline benchmarks.
Highlights
The following figure shows the different Offline and Server performance scenarios in the data center suite. These results provide an overview; follow-up blogs will provide more details about the results.
The following figure shows that these servers delivered excellent performance for all models in the benchmark such as ResNet, RetinaNet, 3D-UNet, RNN-T, BERT, DLRM-v2, GPT-J, Stable Diffusion XL, and Llama 2. Note that different benchmarks operate on varied scales. They have all been showcased in an exponentially scaled y-axis in the following figure:
Figure 1: System throughput for submitted systems for the data center suite.
The following figure shows single-stream and multistream scenario results for the edge for ResNet, RetinaNet, 3D-Unet, RNN-T, BERT 99, GPTJ, and Stable Diffusion XL benchmarks. The lower the latency, the better the results and for Offline scenario, higher the better.
Figure 2: Edge results with PowerEdge XR7620 and XR8620t servers overview
Conclusion
The preceding results were officially submitted to MLCommons. They are MLPerf-compliant results for the Inference v4.0 benchmark across various benchmarks and suites for all the tasks in the benchmark such as image classification, object detection, natural language processing, speech recognition, recommenders, medical image segmentation, LLM 6B and LLM 70B question answering, and text to image. These results prove that Dell PowerEdge XE9680, XE8640, XE9640, and R760xa servers are capable of delivering high performance for inference workloads. Dell Technologies secured several #1 titles that make Dell PowerEdge servers an excellent choice for data center and edge inference deployments. End users can benefit from the plethora of submissions that help make server performance and sizing decisions, which ultimately deliver enterprises’ AI transformation and shows Dell’s commitment to deliver higher performance.
MLCommons Results
https://mlcommons.org/en/inference-datacenter-40/
https://mlcommons.org/en/inference-edge-40/
The preceding graphs are MLCommons results for MLPerf IDs from 4.0-0025 to 4.0-0035 on the closed datacenter, 4.0-0036 to 4.0-0038 on the closed edge, 4.0-0033 in the closed datacenter power, and 4.0-0037 in closed edge power.