Tesla V100: The AI Computing and HPC Powerhouse

The NVIDIA^® Tesla^® V100 accelerator is the world’s highest performing parallel processor, designed to power the most computationally intensive HPC, ArtificiaI Intelligence, and graphics workloads.

The GV100 GPU includes 21.1 billion transistors with a die size of 815 mm2. It is fabricated on a new TSMC 12 nm FFN high performance manufacturing process customized for NVIDIA. GV100 delivers considerably more compute performance, and adds many new features compared to its predecessor, the Pascal GP100 GPU and its architecture family. Further simplifying GPU programming and application porting, GV100 also improves GPU resource utilization. GV100 is an extremely power-efficient processor, delivering exceptional performance per watt. Figure 2 shows Tesla V100 performance for deep learning training and inference using the ResNet-50 deep neural network.

Figure 1: Left: Tesla V100 trains the ResNet-50 deep neural network 2.4x faster than Tesla P100. Right: Given a target latency per image of 7ms, Tesla V100 is able to perform inference using the ResNet-50 deep neural network 3.7x faster than Tesla P100. (Measured on pre-production Tesla V100.)

COMPARISONS:

This table 1 compares NVIDIA^® Tesla^® accelerators over the past 5 years.

Tesla Product	Tesla K40	Tesla M40	Tesla P100	Tesla V100
GPU	GK180 (Kepler)	GM200 (Maxwell)	GP100 (Pascal)	GV100 (Volta)
SMs	15	24	56	80
TPCs	15	24	28	40
FP32 Cores/SM	192	128	64	64
FP32 Cores/GPU	2880	3072	3584	5120
FP64 Cores/SM	64	4	32	32
FP64 Cores/GPU	960	96	1792	2560
Tensor Cores/SM	NA	NA	NA	8
Tensor Cores/GPU	NA	NA	NA	640
GPU Boost Clock	810/875 MHz	1114 MHz	1480 MHz	1455 MHz
Peak FP32 TFLOP/s*	5.04	6.8	10.6	15
Peak FP64 TFLOP/s*	1.68	2.1	5.3	7.5
Peak Tensor Core TFLOP/s*	NA	NA	NA	120
Texture Units	240	192	224	320
Memory Interface	384-bit GDDR5	384-bit GDDR5	4096-bit HBM2	4096-bit HBM2
Memory Size	Up to 12 GB	Up to 24 GB	16 GB	16 GB
L2 Cache Size	1536 KB	3072 KB	4096 KB	6144 KB
Shared Memory Size/SM	16 KB/32 KB/48 KB	96 KB	64 KB	Configurable up to 96 KB
Register File Size/SM	256 KB	256 KB	256 KB	256KB
Register File Size/GPU	3840 KB	6144 KB	14336 KB	20480 KB
TDP	235 Watts	250 Watts	300 Watts	300 Watts
Transistors	7.1 billion	8 billion	15.3 billion	21.1 billion
GPU Die Size	551 mm²	601 mm²	610 mm²	815 mm²
Manufacturing Process	28 nm	28 nm	16 nm FinFET+	12 nm FFN

Key Features

Key compute features of Tesla V100 include the following:

• Tensor Core
  An Exponential Leap in Performance

  New Streaming Multiprocessor (SM) Architecture Optimized for Deep Learning Volta features a major new redesign of the SM processor architecture that is at the center of the GPU. The new Volta SM is 50% more energy efficient than the previous generation Pascal design, enabling major boosts in FP32 and FP64 performance in the same power envelope. New Tensor Cores designed specifically for deep learning deliver up to 12x higher peak TFLOPs for training. With independent, parallel integer and floating point datapaths, the Volta SM is also much more efficient on workloads with a mix of computation and addressing calculations. Volta’s new independent thread scheduling capability enables finer-grain synchronization and cooperation between parallel threads. Finally, a new combined L1 Data Cache and Shared Memory subsystem significantly improves performance while also simplifying programming.

• HBM2 Memory: Faster, Higher Efficiency Volta’s highly tuned 16GB HBM2 memory subsystem delivers 900 GB/sec peak memory bandwidth. The combination of both a new generation HBM2 memory from Samsung, and a new generation memory controller in Volta, provides 1.5x delivered memory bandwidth versus Pascal GP100 and greater than 95% memory bandwidth efficiency running many workloads.
• Volta Multi-Process Service Volta Multi-Process Service (MPS) is a new feature of the Volta GV100 architecture providing hardware acceleration of critical components of the CUDA MPS server, enabling improved performance, isolation, and better quality of service (QoS) for multiple compute applications sharing the GPU. Volta MPS also triples the maximum number of MPS clients from 16 on Pascal to 48 on Volta.
• NVIDIA^® NVLink
  Scalability for Rapid Time-to-Solution

  Second-Generation NVLink™ The second generation of NVIDIA’s NVLink high-speed interconnect delivers higher bandwidth, more links, and improved scalability for multi-GPU and multi-GPU/CPU system configurations. GV100 supports up to 6 NVLink links at 25 GB/s for a total of 300 GB/s. NVLink now supports CPU mastering and cache coherence capabilities with IBM Power 9 CPU-based servers. The new NVIDIA DGX-1 with V100 AI supercomputer uses NVLink to deliver greater scalability for ultra-fast deep learning training.

• Enhanced Unified Memory and Address Translation Services GV100 Unified Memory technology in Volta GV100 includes new access counters to allow more accurate migration of memory pages to the processor that accesses the pages most frequently, improving efficiency for accessing memory ranges shared between processors. On IBM Power platforms, new Address Translation Services (ATS) support allows the GPU to access the CPU’s page tables directly.
• Cooperative Groups and New Cooperative Launch APIs Cooperative Groups is a new programming model introduced in CUDA 9 for organizing groups of communicating threads. Cooperative Groups allows developers to express the granularity at which threads are communicating, helping them to express richer, more efficient parallel decompositions. Basic Cooperative Groups functionality is supported on all NVIDIA GPUs since Kepler. Pascal and Volta include support for new Cooperative Launch APIs that support synchronization amongst CUDA thread blocks. Volta adds support for new synchronization patterns.
• Volta-Optimized Software
  GPU-Accelerated Frameworks and Applications

  Volta Optimized Software New versions of deep learning frameworks such as Caffe2, MXNet, CNTK, TensorFlow, and others harness the performance of Volta to deliver dramatically faster training times and higher multi-node training performance. Volta-optimized versions of GPU accelerated libraries such as cuDNN, cuBLAS, and TensorRT leverage the new features of the Volta GV100 architecture to deliver higher performance for both deep learning and High Performance Computing (HPC) applications. The NVIDIA CUDA Toolkit version 9.0 includes new APIs and support for Volta features to provide even easier programmability.

• Maximum Performance and Maximum Efficiency Modes In Maximum Performance mode, the Tesla V100 accelerator will operate unconstrained up to its TDP (Thermal Design Power) level of 300W to accelerate applications that require the fastest computational speed and highest data throughput. Maximum Efficiency Mode allows data center managers to tune power usage of their Tesla V100 accelerators to operate with optimal performance per watt. A not-to-exceed power cap can be set across all GPUs in a rack, reducing power consumption dramatically, while still obtaining excellent rack performance.

For more information, please contact XENON or call to speak to a solutions architect, 1300 030 888.