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

2025-12-01

AI Summary

GPU architectural advancements—particularly NVIDIA’s Blackwell (B200)—are outpacing the development of rigorous, architecture-specific performance evaluation methodologies. Method: We introduce the first open-source microbenchmark suite tailored to the Blackwell architecture, systematically quantifying the impact of its fifth-generation Tensor Cores, Tensor Memory (TMEM), hardware decompression engines, and dual-die design on compute throughput, memory behavior, and energy efficiency. Benchmarks include dense/sparse GEMM, Transformer inference and training, and multi-precision (FP32–FP4) kernels. Contribution/Results: Experiments reveal that Blackwell achieves 1.56× higher mixed-precision throughput, 42% better energy efficiency, and 58% lower cache miss latency versus the H200. This work provides the first empirical validation of performance gains from Blackwell’s novel hardware units and establishes a foundation for co-designing algorithms and architectures for next-generation GPUs.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Quantify performance of Blackwell GPU architectural innovations
• Develop systematic microbenchmark suite for modern GPU features
• Analyze memory, tensor core, and energy efficiency improvements

Innovation

• Open-source microbenchmark suite for GPU analysis
• Systematic evaluation of tensor core and memory improvements
• Quantifies performance gains and energy efficiency enhancements