(previous generations provided one dedicated FP32 path and one dedicated Integer path) Double FP32 processing throughput with upgraded Streaming Multiprocessors (SM) that support FP32 computation on both datapaths.Visualization “Ampere” GPU architecture – important features and changes: Compute Data Compression accelerates compressible data patterns, resulting in up to 4x faster DRAM bandwidth, up to 4x faster L2 read bandwidth, and up to 2x increase in L2 capacity.Improved L2 Cache is twice as fast and nearly seven times as large as L2 on Tesla V100.Larger and Faster L1 Cache and Shared Memory for improved performance.Native ECC Memory detects and corrects memory errors without any capacity or performance overhead.4th-generation PCI-Express doubles transfer speeds between the system and each GPU.3rd-generation NVLink doubles transfer speeds between GPUs.Multi-Instance GPU allows each A100 GPU to run seven separate/isolated applications.High-speed HBM2 Memory delivers 40GB or 80GB capacity at 1.6TB/s or 2TB/s throughput.Tensor Cores support many instruction types: FP64, TF32, BF16, FP16, I8, I4, B1.Speedups of 7x~20x for inference, with sparse INT8 TensorCores (vs Tesla V100).Speedups of 3x~20x for network training, with sparse TF32 TensorCores (vs Tesla V100).
Sparse matrix optimizations potentially double training and inference performance.TensorFloat 32 (TF32) instructions improve performance without loss of accuracy.Exceptional AI deep learning training and inference performance:.19.5 TFLOPS FP32 single-precision floating-point performance.Up to 19.5 TFLOPS FP64 double-precision via Tensor Core FP64 instruction support.9.7 TFLOPS FP64 double-precision floating-point performance.Computational “Ampere” GPU architecture – important features and changes: The specifications of both versions are shown below – speak with one of our GPU experts for a personalized summary of the options best suited to your needs. Broadly-speaking, there is one version dedicated solely to computation and a second version dedicated to a mixture of graphics/visualization and compute. Note that not all “Ampere” generation GPUs provide the same capabilities and feature sets. Ampere A100 GPUs began shipping in May 2020 (with other variants shipping by end of 2020). “Ampere” GPUs improve upon the previous-generation “Volta” and “Turing” architectures. Finally, we provide a detailed performance analysis of all precision levels on a large number of hardware microarchitectures and show that significant speedup is achieved with mixed FP32/16-bit.This article provides details on the NVIDIA A-series GPUs (codenamed “Ampere”). We find that the difference in accuracy between FP64 and FP32 is negligible in almost all cases, and that for a large number of cases even 16-bit is sufficient. We then carry out an in-depth characterization of LBM accuracy for six different test systems with increasing complexity: Poiseuille flow, Taylor-Green vortices, Karman vortex streets, lid-driven cavity, a microcapsule in shear flow (utilizing the immersed-boundary method), and, finally, the impact of a raindrop (based on a volume-of-fluid approach). Based on this observation, we develop customized 16-bit formats-based on a modified IEEE-754 and on a modified posit standard-that are specifically tailored to the needs of the LBM. For this, we first show that the commonly occurring number range in the LBM is a lot smaller than the FP16 number range. Here we evaluate the possibility to use even FP16 and posit16 (half) precision for storing fluid populations, while still carrying arithmetic operations in FP32. Alongside reduction in memory footprint, significant performance benefits can be achieved by using FP32 (single) precision compared to FP64 (double) precision, especially on GPUs. Fluid dynamics simulations with the lattice Boltzmann method (LBM) are very memory intensive.