high performance computing on graphics processing units: hgpu.org

A. Lonardo, F. Ameli, R. Ammendola, A. Biagioni, O. Frezza, G. Lamanna, F. Lo Cicero, M. Martinelli, P. S. Paolucci, E. Pastorelli, L. Pontisso, D. Rossetti, F. Simeone, F. Simula, M. Sozzi, L. Tosoratto, P. Vicini

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Tags: CUDA, FPGA, Instrumentation and Detectors, nVidia, Physics, Tesla M2070

June 16, 2014 by hgpu

Grover: Looking for Performance Improvement by Disabling Local Memory Usage in OpenCL Kernels

Jianbin Fang, Henk Sips, Pekka Jaaskelainen, Ana Lucia Varbanescu

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Tags: ATI, ATI Radeon HD 7970, Intel Xeon Phi, Local memory, nVidia, OpenCL, Reverse Engineering, Tesla C2050, Tesla K20

June 16, 2014 by jfang

Toward OpenCL Automatic Multi-Device Support

Henry Sylvain, Alexandre Denis, Denis Barthou, Marie-Christine Counilh, Raymond Namyst

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Tags: ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, nVidia, nVidia Quadro FX 5800, OpenCL, Tesla M2075

June 15, 2014 by hgpu

Evaluating CP2K on Exascale Hardware: Intel Xeon Phi

Fiona Reid, Iain Bethune

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Tags: Chemistry, Intel Xeon Phi, Molecular dynamics, MPI, OpenMP

June 15, 2014 by hgpu

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

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high performance computing on graphics processing units: hgpu.org

Applications

An Improved Monte Carlo Ray Tracing for Large-Scale Rendering in Hadoop

A CUDA based Solution to the Multidimensional Knapsack Problem Using the Ant Colony Optimization

HAM – Heterogenous Active Messages for Efficient Offloading on the Intel Xeon Phi

GPU Implementation of Bayesian Neural Network Construction for Data-Intensive Applications

Synergia CUDA: GPU-accelerated accelerator modeling package

Divide and Conquer G-Buffer Ray Tracing

An in-depth performance analysis of irregular workloads on VLIW APU

Improved Distance Weighted GPU-based 3D Ultrasound Reconstruction Methods

NaNet: a Low-Latency, Real-Time, Multi-Standard Network Interface Card with GPUDirect Features

Grover: Looking for Performance Improvement by Disabling Local Memory Usage in OpenCL Kernels

Toward OpenCL Automatic Multi-Device Support

Evaluating CP2K on Exascale Hardware: Intel Xeon Phi

Recent source codes

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

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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