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Table 1: Comparison of key attributes between V100 and A100 GPUsĪmpere dramatically increases each of these key hardware features, including 5x greater FP16 throughput, 2.2x more DRAM bandwidth, and 6.7x more on-chip L2 cache. Table 1 compares key attributes of Ampere A100 (2020) to its predecessor in the datacenter, the Volta V100 (2017) GPU. Leveraging architecture concepts for GEMM (General Matrix-Matrix Multiplication) acceleration, the A100 incorporates Tensor Core support for double-precision FP64 data types, boosting peak GPU performance to 19.5 TFLOPS. Traditional HPC workloads, such as circuit simulations, continue to demand more double-precision compute performance and memory bandwidth. The Ampere A100, launched in 2020, is the most recent NVIDIA GPU.
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The latest version of the PrimeSim simulator (version 2021.09) supports the NVIDIA A100 Tensor Core GPU architecture. Synopsys PrimeSim™ Continuum, released originally on NVIDIA V100 GPUs, offers a unique next-generation CPU-GPU hybrid architecture that delivers significant performance improvements while meeting signoff accuracy requirements for today’s advanced applications. Figure 1: Performance gain with V100 GPU Synopsys PrimeSim Continuum Now with NVIDIA Ampere Tensor Core A100 GPU Across a variety of circuit types (PLLs, SerDes, SRAMs, PHYs) with device counts in the tens or hundreds of millions of elements, GPUs can deliver up to 10x improvement in simulation runtime, as shown in Figure 1. With CPU performance gains beginning to plateau, GPUs are an attractive option to accelerate circuit simulation and sign off. GPUs are an Attractive Option to Accelerate Circuit Simulation and Sign Off To solve for these challenges and ensure that today’s chips are thoroughly verified, you need a unified flow with advanced GPU performance scaling.
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Indeed, the HPC industry is moving towards an accelerated computing model where intensive calculations are carried out on GPUs to achieve faster real-world execution times.Ĭontinuing advances in semiconductor process technology along with increasing circuit complexity are presenting a growing challenge for circuit simulation, specifically when it comes to simulation cost, quality, and time to results. Initially used to render graphics and video, GPUs are increasingly used for high-performance computing (HPC) tasks such as deep learning, artificial intelligence (AI), and more. Over the last decade, GPU technology has experienced phenomenal advancements.
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Whether you’re playing the latest interactive game or watching a movie on your tablet, you’re reaping the benefits of graphics processing units (GPUs).
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By Samad Parekh, Senior Product Manager for Custom Design and Physical Verification Group, Synopsys, and Srinivas Kodiyalam, Senior Developer Relations Manager, Industrial HPC and AI, NVIDIA