Simulation / Modeling / Design

Fueling High-Performance Computing with Full-Stack Innovation

High-performance computing (HPC) has become the essential instrument of scientific discovery. 

Whether it is discovering new, life-saving drugs, battling climate change, or creating accurate simulations of our world, these solutions demand an enormous—and rapidly growing—amount of processing power. They are increasingly out of reach of traditional computing approaches. 

That is why industry has embraced NVIDIA GPU-accelerated computing. Combined with AI, it is bringing millionfold leaps in performance for scientific advancement. Today, 2,700 applications can benefit from NVIDIA GPU acceleration, and that number continues to rise, backed by a growing community of three million developers.

HPC application performance improvements

Delivering the many-fold speedups across the entire breadth of HPC applications takes relentless innovation at every level of the stack. This starts with chips and systems and goes through to the application frameworks themselves. 

The NVIDIA platform continues to deliver significant performance improvements each year, with relentless advancements in architecture and across the NVIDIA software stack. Compared to the P100 released just six years ago, the H100 Tensor Core GPU is expected to deliver an estimated 26x higher performance, more than 3x faster than Moore’s Law. 

Chart shows that the NVIDIA H100 delivers 26x more performance than the NVIDIA P100 released six years ago.
Figure 1. NVIDIA HPC + AI platform performance from P100 to H100
NVIDIA HPC SDK is divided into three segments of function: Development, Analysis, and Deployment, with a number of developer assets offered for each. 
Figure 2. The NVIDIA HPC SDK has developer assets offered for each function. 

Core to the NVIDIA platform is a feature-rich and high-performance software stack. To facilitate GPU acceleration for the widest range of HPC applications, the platform includes the NVIDIA HPC SDK. The SDK provides unmatched developer flexibility, enabling the creation and porting of GPU-accelerated applications using standard languages, directives, and CUDA

The power of the NVIDIA HPC SDK lies in a vast suite of highly optimized GPU-accelerated math libraries, enabling you to harness the full performance potential of NVIDIA GPUs. For the best multi-GPU and multi-node performance, the NVIDIA HPC SDK also provides powerful communications libraries:

Altogether, this platform provides the highest performance and flexibility to support the large and growing universe of GPU-accelerated HPC applications. 

HPC performance and energy efficiency

To showcase how the NVIDIA full-stack innovation translates into the highest performance for accelerated HPC, we compared the performance of a server from HPE with four NVIDIA GPUs with that of a similarly configured server based on an equal number of accelerator modules from another vendor. 

We tested a set of five widely used HPC applications using a wide variety of datasets. While the NVIDIA platform accelerates 2,700 applications spanning every industry, the applications we could use in this comparison were limited by the selection of software and application versions that are available for the other vendor’s accelerators. 

For all workloads except for NAMD, which is software for molecular dynamics simulation, our results are calculated using the geomean of results across multiple datasets to minimize the influence of outliers and to be representative of customer experiences.

We also tested these applications in multi-GPU and single-GPU scenarios. 

In the multi-GPU scenario, with all accelerators in the tested systems being used to run a single simulation, the A100 Tensor Core GPU-based server delivered up to 2.1x higher performance than the alternative offering.

Chart shows the performance of four NVIDIA A100 GPUs compared to four AMD MI250 accelerators across five popular HPC applications. NVIDIA A100 delivers up to 2.1X higher performance.
Figure 3. NVIDIA A100 four-GPU performance comparison

Fueled by continued advances in compute performance, the field of molecular dynamics is moving towards simulating ever-larger systems of atoms for longer periods of simulated time. These advances enable researchers to simulate an increasing set of biochemical mechanisms, such as photosynthetic electron transport and vision signal transduction. These and other processes have long been the subject of scientific debate because they have been beyond the reach of simulation, which is the primary tool for validation. This was due to the prohibitively long amount of time needed to complete the simulations.

However, we recognize that not all users of these applications run them with multiple GPUs per simulation. For optimal throughput, the best execution method is often to assign one GPU per simulation. 

When running these same applications on a single accelerator module—a full GPU on the NVIDIA A100 and both compute dies on the alternative product—the NVIDIA A100-based system delivered up to 1.9x faster performance. 

Chart shows the performance of single NVIDIA A100 compared to single AMD MI250 across five popular HPC applications. NVIDIA A100 delivers up to 1.9X higher performance when running popular HPC applications on a single GPU.
Figure 4. NVIDIA A100 single-GPU performance comparison

Energy costs represent a significant portion of the total cost of ownership (TCO) of data centers and supercomputing centers alike, underscoring the importance of power-efficient computing platforms. Our testing showed that the NVIDIA platform provided up to 2.8x higher throughput-per-watt than the alternative offering.

Chart shows the energy efficiency of four NVIDIA A100 GPUs compared to four AMD MI250s across five popular HPC applications. NVIDIA A100 delivers up to 2.8x higher power efficiency.
Figure 5. NVIDIA A100 power efficiency comparison

Efficiency ratio of A100 to MI250 shown – higher is better for NVIDIA.  Geomean over multiple datasets (varies) per application.  Efficiency is Performance / Power consumption (Watts) as measured for the GPUs using measured using NVIDIA SMI and equivalent functionality in ROCm |

AMD MI250 measured on a MI250 System with (2) AMD EPYC 7763 with 4x AMD Instinct™ MI250 OAM (128 GB  HBM2e) 500W GPUs with AMD Infinity Fabric™ technology. NVIDIA runs on HGX A100 4-GPU server using dual EPYC 7713 CPUs and 4x A100 (80 GB) SXM4

LAMMPS develop_db00b49(AMD) develop_2a35ec2(NVIDIA) datasets ReaxFF/c, Tersoff, Leonard-Jones, SNAP   | NAMD 3.0alpha9 dataset STMV_NVE | OpenMM 7.7.0 Ensemble runs for datasets: amber20-stmv, amber20-cellulose, apoa1pme, pme|

GROMACS 2021.1(AMD) 2022(NVIDIA) datasets  ADH-Dodec (h-bond), STMV (h-bond) | AMBER 20.xx_rocm_mr_202108(AMD) and 20.12-AT_21.12 (NVIDIA) datasets Cellulose_NVE, STMV_NVE | 1x MI250 has 2x GCD

The excellent performance and power efficiency of the NVIDIA A100 GPU is the result of many years of relentless software-hardware co-optimization to maximize application performance and efficiency. For more information about the NVIDIA Ampere architecture, see the NVIDIA A100 Tensor Core GPU whitepaper.

A100 also presents as a single processor to the operating system, requiring that only one MPI rank be launched to take full advantage of its performance. And, A100 delivers excellent performance at scale thanks to the 600-GB/s NVLink connections between all GPUs in a node.

AI and HPC convergence

Just as accelerated computing is bringing many-fold speedups to modeling and simulation applications, the combination of AI and HPC will deliver the next step-function increase in performance to unlock the next wave of scientific discovery. 

In the three years between our first MLPerf training submissions and the most recent results, the NVIDIA platform has delivered 20x more deep learning training performance on this industry-standard, peer-reviewed suite of benchmarks. The gains come from a combination of chip, software, and at-scale improvements.

Chart shows the increases in performance of the NVIDIA platform between successive rounds of MLPerf Training across four networks. Between MLPerf Training v0.5 and MLPerf Training v1.1, NVIDIA delivered up to 20x more performance.
Figure 6. NVIDIA performance gains over three years

Scientists and researchers are already using the power of AI to deliver dramatic improvements in performance, turbocharging scientific discovery:

Supercomputing centers around the world are continuing to adopt accelerated AI supercomputers.

For more information about the latest performance data, see HPC Application Performance.

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