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

Aaron Jarmusch, Sunita Chandrasekaran

TL;DR

This work introduces an open-source PTX/CUDA microbenchmark suite to systematically quantify NVIDIA Blackwell B200 architecture, focusing on TMEM, the Decompression Engine, and fifth-generation Tensor Cores. It provides empirical evaluations across LLM inference/training and scientific workloads, revealing 1.56× mixed-precision throughput gains and 42% energy efficiency improvements over H200, along with a 58% reduction in memory-access latency via TMEM. Key findings include TMEM-enabled reductions in data movement, DE throughput ranging from 42–462 GB/s with stable output bandwidth, and FP4/FP6 precision trade-offs that enable substantial but accuracy-sensitive speedups. The results yield concrete deployment guidelines and contribute to performance modeling and future GPU design directions for memory-intensive and quantized AI workloads.

Abstract

As GPU architectures rapidly evolve to meet the overcoming demands of exascale computing and machine learning, the performance implications of architectural innovations remain poorly understood across diverse workloads. NVIDIA's Blackwell (B200) generation introduce significant architectural advances including the 5th generation tensor cores, tensor memory (TMEM), decompression engine (DE), and dual chips; however systematic methodologies for quantifying these improvements lag behind hardware development cycles. We contribute an open-source microbenchmark suite that offers practical insights into optimizing workloads to fully utilize the rich feature sets of the modern GPU architecture. This work aims to enable application developers make informed architectural decisions and guide future GPU design directions. Our work studies Blackwell GPUs, compares them to H200 generation with regards to the memory subsystem, tensor core pipeline and floating-point precisions (FP32, FP16, FP8, FP6, FP4). Our systematic evaluation of dense/sparse GEMM, transformer inference, and training workloads demonstrate that B200's tensor core enhancements achieves 1.56x higher mixed-precision throughput and 42% better energy efficiency than H200. Our memory analysis reveals 58% reduction in memory access latency in cache-misses, fundamentally changing optimal algorithm design strategies.

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

TL;DR

This work introduces an open-source PTX/CUDA microbenchmark suite to systematically quantify NVIDIA Blackwell B200 architecture, focusing on TMEM, the Decompression Engine, and fifth-generation Tensor Cores. It provides empirical evaluations across LLM inference/training and scientific workloads, revealing 1.56× mixed-precision throughput gains and 42% energy efficiency improvements over H200, along with a 58% reduction in memory-access latency via TMEM. Key findings include TMEM-enabled reductions in data movement, DE throughput ranging from 42–462 GB/s with stable output bandwidth, and FP4/FP6 precision trade-offs that enable substantial but accuracy-sensitive speedups. The results yield concrete deployment guidelines and contribute to performance modeling and future GPU design directions for memory-intensive and quantized AI workloads.

Abstract

As GPU architectures rapidly evolve to meet the overcoming demands of exascale computing and machine learning, the performance implications of architectural innovations remain poorly understood across diverse workloads. NVIDIA's Blackwell (B200) generation introduce significant architectural advances including the 5th generation tensor cores, tensor memory (TMEM), decompression engine (DE), and dual chips; however systematic methodologies for quantifying these improvements lag behind hardware development cycles. We contribute an open-source microbenchmark suite that offers practical insights into optimizing workloads to fully utilize the rich feature sets of the modern GPU architecture. This work aims to enable application developers make informed architectural decisions and guide future GPU design directions. Our work studies Blackwell GPUs, compares them to H200 generation with regards to the memory subsystem, tensor core pipeline and floating-point precisions (FP32, FP16, FP8, FP6, FP4). Our systematic evaluation of dense/sparse GEMM, transformer inference, and training workloads demonstrate that B200's tensor core enhancements achieves 1.56x higher mixed-precision throughput and 42% better energy efficiency than H200. Our memory analysis reveals 58% reduction in memory access latency in cache-misses, fundamentally changing optimal algorithm design strategies.

Paper Structure

This paper contains 29 sections, 2 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Blackwell Architecture
  4. Blackwell Architecture
  5. PTX-Microbenchmark Methodology
  6. Novel Benchmark Design for Blackwell-Specific Features
  7. Tensor Memory (TMEM)
  8. Decompression Engine Characterization
  9. Tensor Core Characterization
  10. Extended Precision Characterization
  11. Workflow Benchmarks
  12. Memory Subsystem
  13. Tensor Memory (TMEM)
  14. Decompression Engine (DE)
  15. GPU Cores Microarchitecture
  16. ...and 14 more sections

Figures (2)

  • Figure 1: NVIDIA Blackwell GPU dual-die design interconnected via NV-HBI.
  • Figure 2: Tensor Core instruction pipeline for tcgen05, wgmma, and Volta/ Ampere architectures.