Microbenchmarking NVIDIA's Blackwell Architecture: An in-depth Architectural Analysis

TL;DR

This paper introduces an open-source microbenchmark suite to analyze NVIDIA's Blackwell GPU architecture, revealing significant performance and efficiency improvements over previous generations for diverse workloads.

Contribution

It provides a systematic methodology and tools for quantifying architectural innovations in modern GPUs, aiding developers and guiding future hardware design.

Findings

Blackwell's tensor cores improve training throughput by up to 1.85x.

Blackwell achieves 32% better energy efficiency than H200.

The microbenchmark suite enables detailed architectural analysis.

Abstract

As GPU architectures rapidly evolve to meet the growing demands of exascale computing and machine learning, the performance implications of architectural innovations remain poorly understood across diverse workloads. NVIDIA Blackwell (B200) introduces significant architectural advances, including fifth-generation tensor cores, tensor memory (TMEM), a decompression engine (DE), and a dual-chip design; however, systematic methodologies for quantifying these improvements lag behind hardware development cycles. We contribute an open-source microbenchmark suite that provides practical insights into optimizing workloads to fully utilize the rich feature sets of modern GPU architectures. This work enables application developers to make informed architectural decisions and guides future GPU design directions. We study Blackwell GPUs and compare them to the H200 generation with respect to the memory subsystem, tensor core pipeline, and floating-point precisions (FP32, FP16, FP8, FP6, FP4). Our systematic evaluation of dense and sparse GEMM, transformer inference, and training workloads shows that B200 tensor core enhancements achieve 1.85x ResNet-50 and 1.55x GPT-1.3B mixed-precision training throughput, with 32 percent better energy efficiency than H200.