Marc Hamilton, VP, Solutions Architecture and Engineering at Nvidia
In this podcast, Marc Hamilton from Nvidia describes how the new Volta GPUs will power the next generation of systems for HPC and AI.
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.
According to Nvidia, the Tesla V100 accelerator is the world’s highest performing parallel processor, designed to power the most computationally intensive HPC, AI, and graphics workloads.
Tesla V100 delivers industry-leading floating-point and integer performance. Peak computation rates (based on GPU Boost clock rate) are:
- 7.5 TFLOP/s of double precision floating-point (FP64) performance;
- 15 TFLOP/s of single precision (FP32) performance;
- 120 Tensor TFLOP/s of mixed-precision matrix-multiply-and-accumulate.
Similar to the previous generation Pascal GP100 GPU, the GV100 GPU is composed of multiple Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs), and memory controllers. A full GV100 GPU consists of six GPCs, 84 Volta SMs, 42 TPCs (each including two SMs), and eight 512-bit memory controllers (4096 bits total). Each SM has 64 FP32 Cores, 64 INT32 Cores, 32 FP64 Cores, and 8 new Tensor Cores. Each SM also includes four texture units.
Volta will be supported by new releases of deep learning frameworks Caffe 2, Microsoft Cognitive Toolkit, MXNet, and TensorFlow, letting users quickly get the most out of Volta’s power.
