mlsanityData to 14 Oct 2026

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

arXiv 1804.06826 ↗First posted 18 Apr 2018cs.DC cs.PFcited by 35

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Abstract (arXiv)

Every year, novel NVIDIA GPU designs are introduced. This rapid architectural and technological progression, coupled with a reluctance by manufacturers to disclose low-level details, makes it difficult for even the most proficient GPU software designers to remain up-to-date with the technological advances at a microarchitectural level. To address this dearth of public, microarchitectural-level information on the novel NVIDIA GPUs, independent researchers have resorted to microbenchmarks-based dissection and discovery. This has led to a prolific line of publications that shed light on instruction encoding, and memory hierarchy's geometry and features at each level. Namely, research that describes the performance and behavior of the Kepler, Maxwell and Pascal architectures. In this technical report, we continue this line of research by presenting the microarchitectural details of the NVIDIA Volta architecture, discovered through microbenchmarks and instruction set disassembly. Additionally, we compare quantitatively our Volta findings against its predecessors, Kepler, Maxwell and Pascal.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2018.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 7; 2024: 6; 2025: 4; 2026: 6.

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  1. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  2. 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,598
  3. 2023Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Surveycited by 72
  4. 2024ThinK: Thinner Key Cache by Query-Driven Pruningcited by 68
  5. 2023XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languagescited by 34
  6. 2023MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Trainingcited by 25
  7. 2023Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networkscited by 22
  8. 2022Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Modelscited by 19
  9. 2025Ditto: Accelerating Diffusion Model via Temporal Value Similaritycited by 18
  10. 2021Post-Training Sparsity-Aware Quantizationcited by 17
  11. 2022Efficient Quantized Sparse Matrix Operations on Tensor Corescited by 16
  12. 2021FastSeq: Make Sequence Generation Fastercited by 13
23 more
  1. 2021APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Corescited by 12
  2. 2024Collage: Light-Weight Low-Precision Strategy for LLM Trainingcited by 10
  3. 2021GPU-Based Homotopy Continuation for Minimal Problems in Computer Visioncited by 8
  4. 2024Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMscited by 7
  5. 2021Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learningcited by 4
  6. 2024Insight Gained from Migrating a Machine Learning Model to Intelligence Processing Unitscited by 3
  7. 2026E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memorycited by 3
  8. 2026AdaSplash-2: Faster Differentiable Sparse Attentioncited by 3
  9. 2024Optimized Speculative Sampling for GPU Hardware Acceleratorscited by 2
  10. 2024Debunking the CUDA Myth Towards GPU-based AI Systemscited by 2
  11. 2025Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progresscited by 2
  12. 2026KernelSight-LM: A Kernel-Level LLM Inference Simulatorcited by 2
  13. 2022A Study on the Intersection of GPU Utilization and CNN Inferencecited by 1
  14. 2023The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networkscited by 1
  15. 2025Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUscited by 1
  16. 2026M100: An Orchestrated Dataflow Architecture Powering General AI Computingcited by 1
  17. 2021On the Noise Stability and Robustness of Adversarially Trained Networks on NVM Crossbarscited by 0
  18. 2021Accelerating Fully Connected Neural Network on Optical Network-on-Chip (ONoC)cited by 0
  19. 2022APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inferencecited by 0
  20. 2023A Theory of I/O-Efficient Sparse Neural Network Inferencecited by 0
  21. 2025Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generationcited by 0
  22. 2026Rigel: Reverse-Engineering the Metal 4.1 Tensor Compute Path on the Apple M4 Max GPUcited by 0
  23. 2026Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4cited by 0