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

Airborne radar clutter simulation using GPU (CUDA)

Priyanka A P, Channabasappa Baligar

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Tags: CUDA, nVidia, nVidia Quadro FX 380, Signal processing

June 15, 2014 by hgpu

A Parallel Algorithm of PCA-SIFT Based on CUDA

Ping Zhang, Yongqi Sun, Hao Shen, Rui Zhang

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GT 240, OpenCV, Pattern recognition, SIFT

June 15, 2014 by hgpu

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

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

Airborne radar clutter simulation using GPU (CUDA)

A Parallel Algorithm of PCA-SIFT Based on CUDA

Recent source codes

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

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

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