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GPU Performance Portability needs Autotuning

Burkhard Ringlein, Thomas Parnell, Radu Stoica

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Tags: AMD Radeon Instinct MI250, ATI, Auto-Tuning, Computer science, CUDA, DSL, HIP, LLM, nVidia, nVidia A100, Performance, performance portability

May 18, 2025 by hgpu

Exploration of Cryptocurrency Mining-Specific GPUs in AI Applications: A Case Study of CMP 170HX

Xing Kangwei

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Tags: AI, Artificial intelligence, Benchmarking, Computer science, CUDA, Finance, nVidia, nVidia CMP 170HX, OpenCL, Performance

May 18, 2025 by hgpu

Dynamic Memory Management on GPUs with SYCL

Russell K. Standish

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Tags: Computer science, CUDA, HIP, Memory model, nVidia, Package, Performance, SYCL

May 4, 2025 by hgpu

Efficient allocation of image recognition and LLM tasks on multi-GPU system

Marcin Lawenda, Krzesimir Samborski, Kyrylo Khloponin, Łukasz Szustak

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Tags: Computer science, CUDA, Data parallelism, Image recognition, LLM, Machine learning, nVidia, nVidia H100, Performance

March 30, 2025 by hgpu

LLMPerf: GPU Performance Modeling meets Large Language Models

Khoi N.M. Nguyen, Hoang Duy Nguyen Do, Huyen Thao Le, Thanh Tuan Dao

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Tags: Computer science, LLM, Machine learning, nVidia, OpenCL, Performance, Tesla V100

March 23, 2025 by hgpu

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

Lingqi Zhang, Jiajun Huang, Sheng Di, Satoshi Matsuoka, Mohamed Wahib

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Tags: Computer science, CUDA, nVidia, nVidia A100, Performance

March 10, 2025 by hgpu

pyATF: Constraint-Based Auto-Tuning in Python

Richard Schulze, Sergei Gorlatch, Ari Rasch

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Tags: Auto-Tuning, Compilers, Computer science, CUDA, nVidia A100, OpenCL, Package, Performance, Python

March 3, 2025 by hgpu

Seamless acceleration of Fortran intrinsics via AMD AI engines

Nick Brown, Gabriel Rodríguez Canal

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Tags: AI, AMD, Computer science, Fortran, Linear Algebra, Package, Performance

February 24, 2025 by hgpu

Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment

Ben Dong, Qian Wang

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Tags: Benchmarking, Cloud, Computer science, LLM, nVidia, nVidia A100, Performance, Security

February 24, 2025 by hgpu

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Robert Tjarko Lange, Aaditya Prasad, Qi Sun, Maxence Faldor, Yujin Tang, David Ha

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Tags: AI, Computer science, CUDA, LLM, nVidia, nVidia H100, Package, Performance

February 24, 2025 by hgpu

Compiler Support for Speculation in Decoupled Access/Execute Architectures

Robert Szafarczyk, Syed Waqar Nabi, Wim Vanderbauwhede

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Tags: Compilers, Computer science, HLS, Performance, Prefetch

February 10, 2025 by hgpu

On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George, UmaMaheswari C Devi

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia V100, Performance

February 3, 2025 by hgpu

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

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high performance computing on graphics processing units: hgpu.org

GPU Performance Portability needs Autotuning

Exploration of Cryptocurrency Mining-Specific GPUs in AI Applications: A Case Study of CMP 170HX

Dynamic Memory Management on GPUs with SYCL

Efficient allocation of image recognition and LLM tasks on multi-GPU system

LLMPerf: GPU Performance Modeling meets Large Language Models

Can Tensor Cores Benefit Memory-Bound Kernels? (No!)

pyATF: Constraint-Based Auto-Tuning in Python

Seamless acceleration of Fortran intrinsics via AMD AI engines

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Compiler Support for Speculation in Decoupled Access/Execute Architectures

On the Partitioning of GPU Power among Multi-Instances

Recent source codes

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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