Revving up Deep Learning workloads with 2nd Generation Intel Xeon Scalable Processors

Revving up Deep Learning workloads with 2nd Generation Intel Xeon Scalable Processors

The article was written by Lucas Wilison and Frank Han of Dell EMC HPC & AI Innovation Lab in May 2019.

HPC is continuously evolving, and therefore, so are the workloads. As data sets become larger and more complex, AI workloads such as Deep Learning are taking centerstage. More and more Deep Learning workloads are now being run on top of powerful, scalable, low latency HPC systems solving challenges that were unimagined a decade ago. Traditional HPC workloads in Life Sciences, Digital Manufacturing, Oil & Gas, Finance and so on are being fueled by Deep Learning to generate more meaningful insights from data in less time and with more accuracy. Intel recognizes that AI methods are now critical components of HPC workloads. To address the need to both train and, more importantly, facilitate faster decision-making for AI models, Intel has put these workloads front and center with the new 2nd Generation Intel® Xeon® Scalable processor line.

2nd Generation Intel® Xeon® Scalable Processors

2nd Generation Intel® Xeon® Scalable processors bring a host of new and improved capabilities including the ability to deploy Intel® Optane™ DC Persistent Memory, improved DRAM speeds, greater processing capability for traditional instruction sets such as single precision FP32, and new processing capability for Deep Learning workloads with the new Intel® Deep Learning Boost instruction set.

Deep Learning Boost on 2nd Generation Intel® Xeon® Scalable Processors

Deep learning is the process of developing models using artificial neural networks which consist of many independent processing units, or neurons, connected in a dense graph. Neural networks have demonstrated an astonishing ability to identify unknown or unforeseen patterns in all sorts of data and have been applied to domains ranging from image and video recognition and analysis, to audio and language transformation, to time-series data and anomaly detection analysis.

The process of using neural networks for developing cutting-edge models is broken into two phases: training, where existing data is used to teach the neural network how to identify patterns; and inference, where the trained model is exposed to new data and expected to make appropriate decisions. And while the process of training neural networks has been the focus of hardware and software innovation for several years, it is in the inference that businesses are receiving benefit from their AI efforts.

Inference has different hardware requirements than training. Training requires half-precision or single-precision floating point arithmetic and the ability to process many large vectors of similar data simultaneously. Inference has much lower total compute requirements, is focused more heavily on latency (time-to-decision) and can take advantage of lower-precision numerical formats such as 8-bit and 16-bit integers.

The 2nd Generation Intel® Xeon® Scalable processor line focuses primarily on this second (inference) phase with an entirely new capability known as Deep Learning Boost. Intel® Deep Learning Boost brings reduced precision arithmetic (8-bit and 16-bin integers) to Xeon’s 512-bit wide vector units (AVX512). This is a huge capability for reduced precision inference because Deep Learning Boost-enabled Intel® Xeon® processors can simultaneously process 64 8-bit integers (or 32 16-bit integers) in a single hardware instruction! Couple this with the ability to perform fused operations, such as Fused Multiply Add (FMA) on these wide low-precision vectors, and the throughput of the system goes up substantially.

Dell EMC has been benchmarking the realizable performance improvements that Intel® Deep Learning Boost can bring the neural network inference. The figure above shows how much improvement your organization could realize by deploying 2nd Generation Intel® Xeon® Scalable processors with Intel® Deep Learning Boost. While 1st Generation Intel® Xeon® Scalable processors (codenamed "Skylake") are capable of processing 258 images per second on the ResNet-50 inference benchmark in single-precision (FP32), and 389 image per second in reduced 8-bit integer precision, the new instructions that Deep Learning Boost brings to 2nd Generation Intel® Xeon® Scalable processors can more than triple the throughput in 8-bit integer precision to 1278 images per second!

Why This Matters

What does this mean for your business? Each inference your AI model makes is an insight you didn’t have before, or a workload you’ve automated that removes a barrier to a decision. Each of those insights – each of those removed barriers – can translate to a new sale, an additional upsell, or a faster investment decision. That is money in your company’s pockets.

As companies undergo digital transformation, making use of AI – and Deep Dearning specifically – will be critical to remain competitive in a data-driven world. And while training AI models has been the focus in this early stage, inference is the way in which businesses will truly realize the benefits of AI. Dell EMC PowerEdge servers powered by 2nd Generation Intel® Xeon® Scalable processors with Intel® Deep Learning Boost can help your business realize the full potential of AI through higher performance model inference. And higher performance translates to better business.

Quick Tips content is self-published by the Dell Support Professionals who resolve issues daily. In order to achieve a speedy publication, Quick Tips may represent only partial solutions or work-arounds that are still in development or pending further proof of successfully resolving an issue. As such Quick Tips have not been reviewed, validated or approved by Dell and should be used with appropriate caution. Dell shall not be liable for any loss, including but not limited to loss of data, loss of profit or loss of revenue, which customers may incur by following any procedure or advice set out in the Quick Tips.

Article ID: SLN317394

Last Date Modified: 05/28/2019 03:50 PM

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