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Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models

Siyuan Chen, Zhichao Lu, Qingfu Zhang

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Tags: Computer science, CUDA, LLM, nVidia, Software Engineering

September 21, 2025 by hgpu

High Performance GPU Implementation of KNN Algorithm: A Review

Pooja Bidye, Pradnya Borkar, Nitin Rakesh

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Tags: Computer science, Machine learning, Nearest neighbour, nVidia, Review

September 21, 2025 by hgpu

Towards Calculating HPC CUDA Kernel Performance on Nvidia GPUs

Dumeni Manatschal

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 3080, Performance, PTX, Thesis

September 14, 2025 by hgpu

An HPC Benchmark Survey and Taxonomy for Characterization

Andreas Herten, Olga Pearce, Filipe S. M. Guimarães

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Tags: Benchmarking, Computer science, CUDA, Fortran, HIP, HPC, MPI, OpenACC, OpenCL, OpenMP, Package, Performance, ROCm, SYCL

September 14, 2025 by hgpu

Home-made Diffusion Model from Scratch to Hatch

Shih-Ying Yeh

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce RTX 5090, Package, Python, PyTorch

September 14, 2025 by hgpu

High Performance Matrix Multiplication

Ethan Davis

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Package, Performance, Python, Tesla V100

September 14, 2025 by hgpu

Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software

Siddharth Samsi, Dan Campbell, Emanuel Scoullos, Oded Green

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, nVidia A100, nVidia H100, nVidia H200, Sensing

September 14, 2025 by hgpu

CrossTL: A Universal Programming Language Translator with Unified Intermediate Representation

Nripesh Niketan, Vaatsalya Shrivastva

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Tags: Compilers, Computer science, CUDA, DirectX, GLSL, HIP, HLSL, nVidia, OpenGL, Package, Programming Languages, Vulkan

September 7, 2025 by hgpu

AnnotationGym: A Generic Framework for Automatic Source Code Annotation

Hafsah Shahzad, Ahmed Sanaullah, Sanjay Arora, Ulrich Drepper, Martin Herbordt

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Tags: Benchmarking, Compilers, Computer science, FPGA, HLS, OpenACC, OpenMP

September 7, 2025 by hgpu

Harnessing Batched BLAS/LAPACK Kernels on GPUs for Parallel Solutions of Block Tridiagonal Systems

David Jin, Alexis Montoison, Sungho Shin

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Tags: AMD Radeon Instinct MI300X, ATI, Benchmarking, BLAS, Computer science, CUDA, Factorization, Julia, nVidia, nVidia H200, Package, ROCm

September 7, 2025 by hgpu

Managing Multi Instance GPUs for High Throughput and Energy Savings

Abhijeet Saraha, Yuanbo Li, Chris Porter, Santosh Pande

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Tags: Computer science, CUDA, Energy-efficient computing, LLM, nVidia, nVidia A100, PyTorch, Security

September 7, 2025 by hgpu

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

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high performance computing on graphics processing units: hgpu.org

Applications

Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models

High Performance GPU Implementation of KNN Algorithm: A Review

Towards Calculating HPC CUDA Kernel Performance on Nvidia GPUs

An HPC Benchmark Survey and Taxonomy for Characterization

Home-made Diffusion Model from Scratch to Hatch

High Performance Matrix Multiplication

Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software

CrossTL: A Universal Programming Language Translator with Unified Intermediate Representation

AnnotationGym: A Generic Framework for Automatic Source Code Annotation

Harnessing Batched BLAS/LAPACK Kernels on GPUs for Parallel Solutions of Block Tridiagonal Systems

Managing Multi Instance GPUs for High Throughput and Energy Savings

Recent source codes

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

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