high performance computing on graphics processing units: hgpu.org

Federico Simmross-Wattenberg, Manuel Rodriguez-Cayetano, Javier Royuela-del-Val, Elena Martin-Gonzalez, Elisa Moya-Saez, Marcos Martin-Fernandez, Carlos Alberola-Lopez

View

Download (PDF)

Source codes

Tags: Heterogeneous systems, Image processing, Medicine, OpenCL, Package

August 5, 2018 by hgpu

CloudCL: Single-Paradigm Distributed Heterogeneous Computing for Cloud Infrastructures

Max Plauth, Florian Roesler, Andreas Polze

View

Download (PDF)

Source codes

Tags: Cloud, Computer science, Java, MPI, nVidia, nVidia GRID K520, OpenCL, Package

July 15, 2018 by hgpu

Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Hugh Leather, Alastair Murray

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, Deep learning, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 780, OpenCL, Package

July 1, 2018 by hgpu

Introducing Parallelism to the Ranges TS

Gordon Brown, Christopher Di Bella, Michael Haidl, Toomas Remmelg, Ruyman Reyes, Michel Steuwer

View

Download (PDF)

Source codes

Tags: Computer science, Heterogeneous systems, OpenCL, Package, SYCL

June 28, 2018 by hgpu

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

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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

Exploiting Task Parallelism with OpenCL: A Case Study

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

Efficient and Scalable k-Means on GPUs

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

Kernel Tuner: A search-optimizing GPU code auto-tuner

A Case Study in Using OpenCL on FPGAs: Creating an Open-Source Accelerator of the AutoDock Molecular Docking Software

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices

Compiler Fuzzing through Deep Learning

Introducing Parallelism to the Ranges TS

Recent source codes

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

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

Most viewed papers (last 30 days)