MD Simulation of GROMACS with AMD EPYC 7003 Series Processors on Dell EMC PowerEdge Servers
Thu, 19 Aug 2021 20:06:53 -0000|
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AMD has recently announced and launched its third generation 7003 series EPYC processors family (code named Milan). These processors build upon the proceeding generation 7002 series (Rome) processors and improve L3 cache architecture along with an increased memory bandwidth for workloads such as High Performance Computing (HPC).
The Dell EMC HPC and AI Innovation Lab has been evaluating these new processors with Dell EMC’s latest 15G PowerEdge servers and will report our initial findings for the molecular dynamics (MD) application GROMACs in this blog.
Given the enormous health impact of the ongoing COVID-19 pandemic, researchers and scientists are working closely with the HPC and AI Innovation Lab to obtain the appropriate computing resources to improve the performance of molecular dynamics simulations. Of these resources, GROMACS is an extensively used application for MD simulations. It has been evaluated with the standard datasets by combining the latest AMD EPYC Milan processor (based on Zen 3 cores) with Dell EMC PowerEdge servers to get most out of the MD simulations.
In a previous blog, Molecular Dynamic Simulation with GROMACS on AMD EPYC- ROME, we published benchmark data for a GROMACS application study on a single node and multinode with AMD EPYC ROME based Dell EMC servers.
The results featured in this blog come from the test bed described in the following table. We performed a single-node and multi-node application study on Milan processors, using the latest AMD stack shown in Table 1, with GROMACS 2020.4 to understand the performance improvement over the older generation processor (Rome).
Table 1: Testbed hardware and software details
Dell EMC PowerEdge 2-socket servers
(with AMD Milan processors)
Dell EMC PowerEdge 2-socket servers
(with AMD Rome processors)
Frequency (Base-Boost )
Processor bus speed
2.45 GHz – 3.5 GHz
2.6 GHz – 3.3 GHz
Processor bus speed
2.0 GHz – 3.675 GHz
2.0 GHz – 3.35 GHz
Processor bus speed
2.8 GHz – 3.7 GHz
2.9 GHz – 3.4 GHz
Red Hat Enterprise Linux 8.3 (4.18.0-240.el8.x86_64)
Red Hat Enterprise Linux 7.8
DDR4 256 G (16 GB x 16) 3200 MT/s
2.0.2 / 1.1.12
NVIDIA Mellanox HDR
NVIDIA Mellanox HDR 100
Table 2: Benchmark datasets used for GROMACS performance evaluation
1536 K and 3072 K
1400 K and 3000 K
Prace – Lignocellulose
The following information describes the performance evaluation for the processor stack listed in the Table 1.
Rome processors compared to Milan processors (GROMACS)
Figure 1: GROMACS performance comparison with AMD Rome processors
For performance benchmark comparisons, we selected Rome processors that are closest to their Milan counterparts in terms of hardware features such as cache size, TDP values, and Processor Base/Turbo Frequency, and marked the maximum value attained for Ns/day by each of the datasets mentioned in Table 2.
Figure 1 shows a 32C Milan processor has higher performance improvements (19 percent for water 1536, 21 percent for water 3072, and 10 to approximately 12 percent with HECBIO sim and lingo cellulose datasets) compared to a 32C Rome processor. This result is due to a higher processor speed and improved L3 cache, wherein more data can be accessed by each core.
Next, with the higher end processor we see only 10 percent gain with respect to the water dataset, as they are more memory intensive. Some percentage is added on due to improvement of frequency for the remaining datasets. Overall, the Milan processor results demonstrated a substantial performance improvement for GROMACS over Rome processors.
Milan processors comparison (32C processors compared to 64C processors)
Figure 2: GROMACS performance with Milan processors
Figure 2 shows performance relative to the performance obtained on the 7543 processor. For instance, the performance of water 1536 is improved from the 32C processor to the 64 core (64C) processor from 41 percent (7713 processor) to 57 percent (7763 processor). The performance improvement is due to the increasing core counts and higher CPU core frequency performance improvement. We observed that GROMACS is frequency sensitive, but not to a great extent. Greater gains may be seen when running GROMACS across multiple ensembles runs or running dataset with higher number of atoms.
We recommend that you compare the price-to-performance ratio before choosing the processor based on the datasets with higher CPU core frequency, as the processors with a higher number of lower-frequency cores may provide better total performance.
Multi-node study with 7713 64C processors
Figure 3: Multi-node study with 7713 64c SKUs
For multi-node tests, the test bed was configured with an NVIDIA Mellanox HDR interconnect running at 200 Gbps and each server included an AMD EPYC 7713 processor. We achieved the expected linear performance scalability for GROMACS of up to four nodes and across each of the datasets. All cores in each server were used while running the benchmarks. The performance increases are close to linear across all the dataset types as core count increases.
For the various datasets we evaluated, GROMACS exhibited strong scaling and was compute intensive. We recommend a processor with high core count for smaller datasets (water 1536, hec 1400); larger datasets (water 3072, ligno,HEC 3000) would benefit from memory per core. Configuring the best BIOS options is important to get the best performance out of the system.
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HPC Application Performance on Dell PowerEdge R7525 Servers with the AMD Instinct™ MI210 GPU
Mon, 12 Sep 2022 12:11:52 -0000|
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PowerEdge support and performance
The PowerEdge R7525 server can support three AMD Instinct™ MI210 GPUs; it is ideal for HPC Workloads. Furthermore, using the PowerEdge R7525 server to power AMD Instinct MI210 GPUs (built with the 2nd Gen AMD CDNA™ architecture) offers improvements on FP64 operations along with the robust capabilities of the AMD ROCm™ 5 open software ecosystem. Overall, the PowerEdge R7525 server with the AMD Instinct MI210 GPU delivers expectational double precision performance and leading total cost of ownership.
Figure 1: Front view of the PowerEdge R7525 server
We performed and observed multiple benchmarks with AMD Instinct MI210 GPUs populated in a PowerEdge R7525 server. This blog shows the performance of LINPACK and the OpenMM customizable molecular simulation libraries with the AMD Instinct MI210 GPU and compares the performance characteristics to the previous generation AMD Instinct MI100 GPU.
The following table provides the configuration details of the PowerEdge R7525 system under test (SUT):
Table 1. SUT hardware and software configurations
AMD EPYC 7713 64-Core Processor
Ubuntu 20.04.3 LTS
Processor Settings > Logical Processors
NUMA node per socket
The following table contains the specifications of AMD Instinct MI210 and MI100 GPUs:
Table 2: AMD Instinct MI100 and MI210 PCIe GPU specifications
AMD Instinct MI210
AMD Instinct MI100
Peak Engine Clock (MHz)
Peak FP64 (TFlops)
Peak FP64 Tensor DGEMM (TFlops)
Peak FP32 (TFlops)
Peak FP32 Tensor SGEMM (TFlops)
Memory size (GB)
Peak Memory Bandwidth (GB/s)
Memory ECC support
High-Performance LINPACK (HPL)
HPL measures the floating-point computing power of a system by solving a uniformly random system of linear equations in double precision (FP64) arithmetic, as shown in the following figure. The HPL binary used to collect results was compiled with ROCm 5.1.3.
Figure 2: LINPACK performance with AMD Instinct MI100 and MI210 GPUs
The following figure shows the power consumption during a single HPL run:
Figure 3: LINPACK power consumption with AMD Instinct MI100 and MI210 GPUs
We observed a significant improvement in the AMD Instinct MI210 HPL performance over the AMD Instinct MI100 GPU. The numbers on a single GPU test of MI210 are 18.2 TFLOPS which is approximately 2.7 times higher than MI100 number (6.75 TFLOPS). This improvement is due to the AMD CDNA2 architecture on the AMD Instinct MI210 GPU, which has been optimized for FP64 matrix and vector workloads. Also, the MI210 GPU has larger memory, so the problem size (N) used here is large in comparison to the AMD Instinct MI100 GPU.
As shown in Figure 2, the AMD Instinct MI210 has shown almost linear scalability in the HPL values on single node multi-GPU runs. The AMD Instinct MI210 GPU reports better scalability compared to its last generation AMD Instinct MI100 GPUs. Both GPUs have the same TDP, with the AMD Instinct MI210 GPU delivering three times better performance. The performance per watt value of a PowerEdge R7525 system is three times more. Figure 3 shows the power consumption characteristics in one HPL run cycle.
OpenMM is a high-performance toolkit for molecular simulation. It can be used as a library or as an application. It includes extensive language bindings for Python, C, C++, and even Fortran. The code is open source and actively maintained on GitHub and licensed under MIT and LGPL.
Figure 4: OpenMM double-precision performance with AMD Instinct MI100 and MI210 GPUs
Figure 5: OpenMM single-precision performance with AMD Instinct MI100 and MI210 GPUs
Figure 6: OpenMM mixed-precision performance with AMD Instinct MI100 and MI210 GPUs
We tested OpenMM with seven datasets to validate double, single, and mixed precision. We observed exceptional double precision performance with OpenMM on the AMD Instinct MI210 GPU compared to the AMD Instinct MI100 GPU. This improvement is due to the AMD CDNA2 architecture on the AMD Instinct MI210 GPU, which has been optimized for FP64 matrix and vector workloads.
The AMD Instinct MI210 GPU shows an impressive performance improvement in FP64 workloads. These workloads benefit as AMD has doubled the width of their ALUs to a full 64-bits wide. This change allows the FP64 operations to now run at full speed in the new 2nd Gen AMD CDNA architecture. The applications and workloads that are designed to run on FP64 operations are expected to take full advantage of the hardware.
HPC Application Performance on Dell PowerEdge R7525 Servers with NVIDIA A100 GPGPUs
Tue, 24 Nov 2020 17:49:03 -0000|
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The Dell PowerEdge R7525 server powered with 2nd Gen AMD EPYC processors was released as part of the Dell server portfolio. It is a 2U form factor rack-mountable server that is designed for HPC workloads. Dell Technologies recently added support for NVIDIA A100 GPGPUs to the PowerEdge R7525 server, which supports up to three PCIe-based dual-width NVIDIA GPGPUs. This blog describes the single-node performance of selected HPC applications with both one- and two-NVIDIA A100 PCIe GPGPUs.
The NVIDIA Ampere A100 accelerator is one of the most advanced accelerators available in the market, supporting two form factors:
- PCIe version
- Mezzanine SXM4 version
The PowerEdge R7525 server supports only the PCIe version of the NVIDIA A100 accelerator.
The following table compares the NVIDIA A100 GPGPU with the NVIDIA V100S GPGPU:
NVIDIA A100 GPGPU
NVIDIA V100S GPGPU
Peak memory bandwidth
Up to 1555 GB/s
Up to 900 GB/s
Up to 1134 GB/s
Total board power
The NVIDIA A100 GPGPU brings innovations and features for HPC applications such as the following:
- Multi-Instance GPU (MIG)—The NVIDIA A100 GPGPU can be converted into as many as seven GPU instances, which are fully isolated at the hardware level, each using their own high-bandwidth memory and cores.
- HBM2—The NVIDIA A100 GPGPU comes with 40 GB of high-bandwidth memory (HBM2) and delivers bandwidth up to 1555 GB/s. Memory bandwidth with the NVIDIA A100 GPGPU is 1.7 times higher than with the previous generation of GPUs.
The following table shows the PowerEdge R7525 server configuration that we used for this blog:
2nd Gen AMD EPYC 7502, 32C, 2.5Ghz
512 GB (16 x 32 GB @3200MT/s)
Either of the following:
2 x NVIDIA A100 PCIe 40 GB
2 x NVIDIA V100S PCIe 32 GB
CentOS Linux release 8.1 (4.18.0-147.el8.x86_64)
11.0 (Driver version - 450.51.05)
The following sections provide our benchmarks results with observations.
High-Performance Linpack benchmark
High Performance Linpack (HPL) is a standard HPC system benchmark. This benchmark measures the compute power of the entire cluster or server. For this study, we used HPL compiled with NVIDIA libraries.
The following figure shows the HPL performance comparison for the PowerEdge R7525 server with either NVIDIA A100 or NVIDIA V100S GPGPUs:
Figure1: HPL performance on the PowerEdge R7525 server with the NVIDIA A100 GPGPU compared to the NVIDIA V100SGPGPU
The problem size (N) is larger for the NVIDIA A100 GPGPU due to the larger capacity of GPU memory. We adjusted the block size (NB) used with the:
- NVIDIA A100 GPGPU to 288
- NVIDIA V100S GPGPU to 384
The AMD EPYC processors provide options for multiple NUMA combinations. We found that the best value of 4 NUMA per socket (NPS=4), with NUMA per socket 1 and 2 lower the performance by 10 percent and 5 percent respectively. In a single PowerEdge R7525 node, the NVIDIA A100 GPGPU delivers 12 TF per card using this configuration without an NVLINK bridge. The PowerEdge R7525 server with two NVIDIA A100 GPGPUs delivers 2.3 times higher HPL performance compared to the NVIDIA V100S GPGPU configuration. This performance improvement is credited to the new double-precision Tensor Cores that accelerate FP64 math.
The following figure shows power consumption of the server while running HPL on the NVIDIA A100 GPGPU in a time series. Power consumption was measured with an iDRAC. The server reached 1038 Watts at peak due to a higher GFLOPS number.
Figure2: Power consumption while running HPL
High Performance Conjugate Gradient benchmark
The High Performance Conjugate Gradient (HPCG) benchmark is based on a conjugate gradient solver, in which the preconditioner is a three-level hierarchical multigrid method using the Gauss-Seidel method.
As shown in the following figure, HPCG performs at a rate 70 percent higher with the NVIDIA A100 GPGPU due to higher memory bandwidth:
Figure 3: HPCG performance comparison
Due to different memory size, the problem size used to obtain the best performance on the NVIDIA A100 GPGPU was 512 x 512 x 288 and on the NVIDIA V100S GPGPU was 256 x 256 x 256. For this blog, we used NUMA per socket (NPS)=4 and we obtained results without an NVLINK bridge. These results show that applications such as HPCG, which fits into GPU memory, can take full advantage of GPU memory and benefit from the higher memory bandwidth of the NVIDIA A100 GPGPU.
In addition to these two basic HPC benchmarks (HPL and HPCG), we also tested GROMACS, an HPC application. We compiled GROMACS 2020.4 with the CUDA compilers and OPENMPI, as shown in the following table:
Figure4: GROMACS performance with NVIDIA GPGPUs on the PowerEdge R7525 server
The GROMACS build included thread MPI (built in with the GROMACS package). All performance numbers were captured from the output “ns/day.” We evaluated multiple MPI ranks, separate PME ranks, and different nstlist values to achieve the best performance. In addition, we used settings with the best environment variables for GROMACS at runtime. Choosing the right combination of variables avoided expensive data transfer and led to significantly better performance for these datasets.
GROMACS performance was based on a comparative analysis between NVIDIA V100S and NVIDIA A100 GPGPUs. Excerpts from our single-node multi-GPU analysis for two datasets showed a performance improvement of approximately 30 percent with the NVIDIA A100 GPGPU. This result is due to improved memory bandwidth of the NVIDIA A100 GPGPU. (For information about how the GROMACS code design enables lower memory transfer overhead, see Developer Blog: Creating Faster Molecular Dynamics Simulations with GROMACS 2020.)
The Dell PowerEdge R7525 server equipped with NVIDIA A100 GPGPUs shows exceptional performance improvements over servers equipped with previous versions of NVIDIA GPGPUs for applications such as HPL, HPCG, and GROMACS. These performance improvements for memory-bound applications such as HPCG and GROMACS can take advantage of higher memory bandwidth available with NVIDIA A100 GPGPUs.