Summary

AMD will be releasing their next-gen GPU, the MI100, in December 2020. This new technology targets to accelerate HPC and ML workloads across various industries. This DfD will discuss the performance metrics and general improvements to the MI100 GPU with the intention of educating customers on how they can best utilize the technology to accelerate their own needs and goals.

PowerEdge Support and Performance

The AMD Instinct MI100 GPU will be best powered by the PowerEdge R7525, which can currently support 3 MI100s. The R7525 with MI100 is the only platform with PCIe Gen4 capability, making it ideal for HPC workloads. Both the MI100 GPUs and Rome processors have a large number of cores, making them a great fit for related computing workloads like AI/ML/DL. Furthermore, using the PE R7525 to power MI100 GPUs will offer increased memory bandwidth from the support of up to eight memory channels. Overall, customers can expect great SP performance and leading TCO from the AMD MI100.

Multiple benchmarks were performed and observed with MI100 GPUs populated in a PowerEdge R7525 server. The first is the LAMMPS benchmark, which measures the performance and scalability of parallel molecular dynamic simulations. Figure 1 below shows very fast atom- timesteps per second across four datasets that scale mostly linearly as the number of populated MI100 GPUs increase from one to three.

Figure 1 – LAMMPS benchmark performance for 1, 2 and 3 GPUs on four datasets

Figure 2 below highlights the results of the NAMD benchmark; a parallel molecular dynamics system designed to stress the performance and scaling aspects of the MI100 on the R7525. Because NAMD 3.0 does not scale beyond one GPU, three replica simulations were launched on the same server, one on each GPU, in parallel. The ns/day metric represents the number of MD instances that can be completed in a day (higher is better). Additionally, we observe how this data scales across all datasets.

Figure 2 – NAMD benchmark performance                                               Figure 3 – CPU core dependency on NAMD

Lastly, the NAMD CPU Core Dependency line graph in Figure 3 shows the relative performance differences (shown as a multiplier) for a range of CPU cores. We see that for the STMV dataset, the MI100 exhibited an optimum configuration of four CPU cores per GPU. 

User Implementation Use Cases

HPC Workloads – Scientific computing simulations are typically so complex that FP64 double-precision models are required to translate the mathematics into accurate numeric models. The AMD Instinct™ MI100 is the first GPU to break the 11TF computing barrier (FP64) at 11.3TFLOPs. When these high speeds are coupled with the high bandwidth memory and I/O capabilities offered, the AMD MI100 GPU powered by the Dell EMC PowerEdge R7525 allows for a giant leap in computing performance; perfect for HPC workloads.

AI Workloads – Artificial Intelligence applications typically require FP32 single-precision models to determine high level features from raw input. The AMD Instinct™ MI100 boasts 3.5x the FP32 performance compared to the previous-gen MI50, and nearly a 7x boost for FP16 performance compared to the MI50. Additionally, new matrix core technology offers superior performance for a full range of mixed precision operations (including BF16, INT4, INT8, FP16, FP32 and FP32 matrices) that provides the capability to work with large models and enhance memory-bound operation performance for all AI system requirements.

Price/Performance – The MI100 GPU has positioned itself for optimal price/performance over maximum performance. Although pricing is volatile, at the time of entry to market the MI100 GPU has a leading price per performance for FP64 and FP16 models when compared to competitors. This increase in price per performance will appeal to price-sensitive customers.

MI100 Specifications

At the heart of AMDs Instinct MI100 GPU is the 1^st Gen CDNA (Compute DNA) ‘Arcturus’ architecture, which focuses on computing improvements for HPC and AI server workloads. The number of compute units has effectively double over the previous generation MI50 from 60 to 120. Similarly, the number of stream processors has doubled from 3840 to 7680, allowing for significant increases of FP64 and FP32 performance. At a peak of 11.5 TFLOPS, the FP64 precision model has gained up to a 75% performance increase over the previous MI50. FP32/FP16 precision models have gained a ~70% performance increase. Furthermore, the MI100 supports 32GB of high-bandwidth memory (HBM2) that enables up to 1.2TB/s memory bandwidth. This is 1.23x higher memory bandwidth over the MI50. See Figure 4 for full details:

Figure 4 – Official MI100 specifications pulled from the AMD website

New Feature Sets

In addition to the product specification improvements noted above, the AMD MI100 introduces several key new features that will further accelerate HPC and AI workloads:

• New Matrix Core Technology
  • FP32 Matrix Core for nearly 3.5x faster HPC & AI workloads vs AMD prior gen
  • FP16 Matrix Core for nearly 7x faster AI workloads vs AMD prior gen
  • Support for newer ML operators like bfloat16

• Enhanced RAS for consistent performance & uptime while offering remote manageability capabilities
• AMD ROCm™ Open ecosystem maximizes developer flexibility and productivity

 Conclusion

The AMD MI100 GPU offers significant performance improvements over the prior-gen MI50, as well as new feature sets that were designed to accelerate HPC and ML workloads. The PowerEdge R7525 configured with MI100 GPUs will be enabled to utilize these new capabilities working in concert with other system components to yield best performance.

For additional information on the MI100, please refer to the Dell Technologies blog HPC Application Performance on Dell EMC PowerEdge R7525 Servers with the AMD MI100 Accelerator