NVIDIA H100 GPU Performance Shatters Machine Learning Benchmarks For Model Training

NVIDIA’s Hopper H100 Tensor Core GPU made its first benchmarking look earlier this yr in MLPerf Inference 2.1. Nobody was stunned that the H100 and its predecessor, the A100, dominated each inference workload. The H100 set world data in all of them and NVIDIA is the one firm to have submitted to each workload for each MLPerf spherical.

A couple of weeks in the past, a brand new set of MLCommons coaching outcomes have been launched, this time for MLPerf 2.1 Coaching, which the NVIDIA H100 and A100 additionally dominated.

Sadly, NVIDIA’s dominance of MLPerf benchmarking suites for inference and coaching has deflected submissions and experiences by many necessary AI firms.

The {industry} would profit from the participation of extra organizations as we’ve seen in different sectors like CPUs, it drives competitors and innovation. Broad involvement in benchmarking suites is critical as a result of machine studying is rising exponentially. Virtually each {industry} phase makes use of machine studying for a variety of functions. As utilization will increase, so does mannequin measurement. Since 2018, MLCommons has held testing rounds that alternate between MLPerf Coaching and MLPerf Inference testing rounds.

Within the 4 years between the primary MLPerf take a look at in 2018 and this yr’s outcomes, machine studying mannequin measurement has elevated by 5 orders of magnitude. With the elevated mannequin measurement and bigger information units, standardized instruments like MLPerf Coaching and MLPerf Inference are extra essential than ever. Machine studying mannequin efficiency have to be measured earlier than it may be improved.

MLPerf 2.1 Coaching benchmarks

MLPerf Coaching and MLPerf Inference use the identical eight workloads proven within the above graphic. Mini Go is an exception as a result of it’s only used to guage reinforcement studying. Every benchmark take a look at is outlined by its personal particular dataset and high quality goal. The Key’s how a lot time it takes to coach the mannequin utilizing the desired dataset with the desired high quality goal.

MLPerf is significant to AI and machine studying as a result of it’s an industry-standard benchmark with peer assessment outcomes that gives legitimate comparisons for mannequin coaching and inference. It’s supported by Amazon, Arm, Baidu, Google, Harvard College, Intel, Meta, Microsoft, Stanford College, and the College of Toronto.

A number of single fashions type excessive efficiency, a number of fashions

It is not uncommon for a number of AI fashions to be chained collectively to fulfill a single enter. An instance of multimodal networks is the verbal request within the above graphic. The query requires ten machine studying fashions to provide a solution. Not solely should a number of fashions function sequentially, however it should additionally ship real-time options.

Some cloud providers additionally use a number of networks to ship providers accelerated by NVIDIA GPUs. All of NVIDIA’s networks and software frameworks can be found on its MLPerf repo, on NGC (NVIDIA’s on-line container repository), and its GitHub repo.

A100 and H100 benchmark coaching efficiency

As proven within the MLPerf Coaching 2.1 efficiency chart, H100 supplied as much as 6.7 x extra efficiency for the BERT benchmark in comparison with how the A100 carried out on its first MLPerf submission in 2019.

A100 continues to be producing file outcomes and excessive efficiency with improved efficiency of as much as 2.5X. This acquire is the results of software program optimization. It is going to seemingly be an NVIDIA providing for fairly a while.

H100 superior efficiency on the BERT NLP mannequin is attributed to its Transformer Engine. The A100 doesn’t have a coaching engine. The brand new engine, mixed with NVIDIA Hopper FP8 Tensor Cores, delivers as much as 9x sooner AI coaching and 30x sooner AI inference speedups on massive language fashions than the A100. The H100 is predicated on Hopper structure and makes use of fourth-gen tensor cores.

Coaching pace is essential and crucial due to AI mannequin measurement. NVIDIA’s transformer engine achieves further pace utilizing 16-bit floating-point precision and a brand new 8-bit floating-point information format. This mix will increase Tensor Core throughput by 2x and reduces reminiscence necessities by 2x in comparison with 16-bit floating-point.

These enhancements, plus superior Hopper software program algorithms, pace up AI efficiency and capabilities permitting the H100 to coach fashions inside days or hours as a substitute of months. The sooner a mannequin can transfer into operation, the sooner its ROI can start contributing to the underside line.

The Hopper structure can dynamically decide if FP8 or 16-bit calculations are wanted for accuracy. Because the transformer engine trains layer by layer, it analyzes the information to find out if lowered precision ought to be used. Relying on the diploma of utilization, lowered precision may cause rounding errors which have an effect on mannequin accuracy.

MLPerf coaching exams measure the time to answer, so a mannequin not solely has to run quick, however it additionally has to converge. Due to this fact, it’s important to do not forget that many errors can stop a mannequin from converging.

NVIDIA’s transformer engine know-how was designed for giant transformer-based networks like BERT. Nonetheless, it isn’t restricted to NLP. It may be utilized to different areas, equivalent to steady diffusion.

Steady Diffusion is a deep studying, compute-intensive text-to-image mannequin launched this yr. It may well generate detailed photos or movies conditioned on textual content descriptions. It can be utilized to duties equivalent to inpainting, outpainting, and producing image-to-image translations utilizing a textual content immediate.

Time to coach at scale

NVIDIA A100 was the one platform to run all workloads within the time to coach at scale. NVIDIA was capable of prepare each workload at scale in underneath 5 minutes apart from Mini Go, which took about 17 minutes.

Mini Go makes use of reinforcement studying which may be very compute-intensive. It takes longer to coach the community as a result of it requires enjoying Mini Go turn-by-turn, then rolling it again via the community after every flip.

Coaching at scale demonstrates that A100 stays a stable platform for coaching. H100 is an answer for essentially the most superior fashions, equivalent to language fashions with large datasets and billions of hyperparameters.

Whereas Intel and Habana did not flip in record-setting performances, its participation was nonetheless necessary for the ecosystem and the way forward for MLPerf.

This graphic reveals relative per accelerator speedup normalized to A100. The H100 (in preview) was submitted for each benchmark and scored superior efficiency for every. It was 2.6X sooner than the A100, which has made vital software program features.

Habana Gaudi2 submitted for Resnet-50 and BERT, and Intel’s Sapphire Rapids submitted for DLRM, ResNet-50, and BERT.

Habana Gaudi2 carried out marginally higher than A100 on BERT and about 0.75 higher than A100 for ResNet-50. Intel acquired Habana in late 2019 for $2 billion. Gaudi2 is Habana’s second-generation deep studying processor. It has 24 tensor cores and 96 GB of reminiscence.

Dave Salvator, Director of AI, Benchmarking and Cloud for NVIDIA, is anticipating increased efficiency from the H100 sooner or later.

“The H100 turned in a really compelling efficiency,” he stated. “However sooner or later, we’ll make software program features with the H100 as we did with the A100. That is the primary spherical we’re submitting H100 for coaching, and it received’t be the final.”

HPC MLPerf 2.0 Supercomputing benchmarking

MLPerf HPC 2.0 measures the time to coach supercomputer fashions for scientific functions. Moreover, there may be an optionally available throughput measurement for multi-user supercomputing methods. This spherical was the third iteration of MLPerf HPC. Like MLPerf for coaching and inference, MLPerf HPC is taken into account an industry-standard system efficiency measure for workloads carried out on supercomputers.

For this spherical, 5 of the world’s largest supercomputers submitted 20 outcomes: Dell (first time for submission), Fujitsu/RIKEN, Helmholz AI, NVIDIA, and Texas Superior Computing Heart (TACC).

That is model 2.0 of the benchmarks, nevertheless, there have been no main modifications since these similar three workloads have been run in 1.0. MLPerf HPC benchmarks measure coaching time and throughput for 3 high-performance simulations which have adopted machine studying methods – Cosmoflow, DeepCAM, and OpenCatalyst.

Due to local weather change, a substantial amount of concentrated work is being accomplished on climate and local weather modeling. NVIDIA can also be engaged on a digital twin of the planet known as Earth Two. This large local weather mannequin simulates the complete world.

NVIDIA HPC Platform Efficiency Management

MLPerf HPC 2.0 has two efficiency metrics:

• Robust Scaling measures time and high quality for coaching the dataset. NVIDIA Selene had the bottom coaching time of all submissions for all three benchmarks.
• Weak Scaling measures throughput and high quality for concurrently coaching a number of fashions on the dataset. Once more, NVIDIA educated extra fashions per minute than any submission.
• For CosmoFlow, NVIDIA has made a 9X enchancment in time to coach over two years.

Though the NVIDIA A100 Tensor Core GPU and the NVIDIA DGX-A100 SuperPOD are nearly three years outdated, MLPerf 2.0 efficiency reveals that A100 continues to be the best performing system for coaching HPC use circumstances.

HPC outcomes are for NVIDIA Selene, an implementation of the DGX SuperPOD and display the A100’s potential. Different supercomputing websites utilizing NVIDIA know-how are additionally delivering good efficiency.

Wrapping up

You will need to point out that NVIDIA was the one group to run all AI coaching workloads for this and all earlier MLPerf Coaching and inference rounds. It has delivered constant management outcomes from the primary MLPerf Coaching 0.5 in December 2018 to the most recent MLPerf Coaching 2.1 that was launched a couple of weeks in the past.

For coaching, inference, and HPC, MLPerf has confirmed NVIDIA has the broadest ecosystem assist for all of the deep studying frameworks. It’s advantageous for purchasers that NVIDIA GPUs can be found from all main cloud suppliers and all main methods for on-prem options. These software frameworks enable prospects to deploy options quickly.

NVIDIA has an end-to-end open platform with software program that helps increase the complete potential of its {hardware}. NVIDIA’s full-stack answer consists of software frameworks equivalent to Merlin and Nemo. With Nemo Megatron service, it’s attainable to leverage enormous language fashions utilizing customized datasets.

ANALYST NOTES

1. There are various explanation why mannequin pace is so important for inference and coaching. One missed motive pertains to the need of a number of coaching runs. Constructing a mannequin is an experimental course of that entails trial and error to get the mannequin correctly tuned. The mannequin have to be rerun every time one thing is tweaked to see the outcomes. The flexibility to run the mannequin sooner means extra trials will be run in a given time. That permits an answer to be discovered and deployed extra shortly. The sooner a mannequin will be deployed, the sooner its advantages can contribute to improved operations and its ROI will be generated.
2. MLPerf gives peer-reviewed apples-to-apples comparisons for coaching and inference. It eliminates the necessity to depend on an organization’s cherry-picked stats from its efficiency testing which will or might not be legitimate.
3. NVIDIA works with lots of the prime AI researchers. I spend a lot time reviewing analysis papers on AI and quantum. Loads of AI analysis work makes use of NVIDIA platforms. To date this yr, over 400 preprint analysis papers have been printed on deep studying utilizing NVIDIA know-how. Will probably be attention-grabbing to see future analysis outcomes utilizing the H100 as its availability will increase.
4. Energy consumption is a big subject within the AI ecosystem. Though measurement of energy consumption isn’t at the moment a part of MLPerf Coaching, it’s into account. Nonetheless, energy is a measurement in MLPerf Inference. For MLPerf Inference 2.1 in September, 2,400 energy measurement outcomes have been submitted. For reference, A100 requires 400 watts, and H100 requires 700 watts. When juggling these two figures, consideration must be given to efficiency and pace. BERT is a superb instance as a result of the H100 enjoys a 6X benefit in pace.
5. Just like the A100, the H100 will be partitioned into seven smaller accelerators that may independently run completely different networks. That could be a solution to get optimum utilization out of the half on the inference aspect and scale back the variety of whole GPUs wanted to deploy a number of networks. Ideally, the characteristic is extra helpful for coaching superior fashions, however it additionally has functions on the inference aspect.

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Moor Insights & Technique founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Know-how Group Inc. VI, Dreamium Labs, Groq, Luminar Applied sciences, MemryX, and Movand

Word: Moor Insights & Technique writers and editors could have contributed to this text.