high performance computing on graphics processing units: hgpu.org

Tal Ben-Nun, Linus Groner, Florian Deconinck, Tobias Wicky, Eddie Davis, Johann Dahm, Oliver Elbert, Rhea George, Jeremy McGibbon, Lukas Trümper, Elynn Wu, Oliver Fuhrer, Thomas Schulthess, Torsten Hoefler

View

Download (PDF)

Tags: CUDA, Earth and Space Sciences, nVidia, Python, Tesla A100, Tesla P100, Weather Prediction Model

May 15, 2022 by hgpu

FPGA Acceleration of Structured-Mesh-Based Explicit and Implicit Numerical Solvers using SYCL

K. Kamalakkannan, G.R. Mudalige, I.Z. Reguly, S.A. Fahmy

View

Download (PDF)

Tags: Computer science, Design space exploration, FPGA, nVidia, SYCL, Tesla V100

May 8, 2022 by hgpu

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Zheming Jin

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Graph theory, nVidia, OpenCL, Package, Performance, SYCL

May 8, 2022 by hgpu

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

Chuan-Chi Wang, Chun-Yen Ho, Chia-Heng Tu, Shih-Hao Hung

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, Package, Particle swarm optimization

May 8, 2022 by hgpu

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Quadro GV100, Performance, Tesla A100

May 8, 2022 by hgpu

Analytical Performance Estimation during Code Generation on Modern GPUs

Dominik Ernst, Markus Holzer, Georg Hager, Matthias Knorr, Gerhard Wellein

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, OpenCL, Package, Tesla A100, Tesla V100

May 8, 2022 by hgpu

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

GPU Ray Tracing with Monte Carlo Methods

The Application of AI Technology in GPU Scheduling Algorithm Optimization

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPU-based JSON data processing using structural indexes

Productive Performance Engineering for Weather and Climate Modeling with Python

FPGA Acceleration of Structured-Mesh-Based Explicit and Implicit Numerical Solvers using SYCL

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

Analytical Performance Estimation during Code Generation on Modern GPUs

Recent source codes

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

Most viewed papers (last 30 days)