high performance computing on graphics processing units: hgpu.org

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Performance Analysis of Sobel Edge Filter on Heterogeneous System Using OpenCL

Aruna Dore, Sunitha Lasrado

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Tags: Computer science, Edge detection, Filtering, nVidia, nVidia GeForce GTX 470, OpenCL, Performance

May 13, 2014 by hgpu

Dynamic Orchestration of Massively Data Parallel Execution

Mehrzad Samadi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 560, OpenCL, Performance, Tesla C2050, Thesis

May 10, 2014 by hgpu

Fine-grain Task Aggregation and Coordination on GPUs

Marc S. Orr, Bradford M. Beckmann, Steven K. Reinhardt, David A. Wood

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Tags: Computer science, GPGPU-sim, Hardware Architecture, OpenCL, Performance

May 10, 2014 by hgpu

Improving the Programmability of GPU Architectures

Cedric Nugteren

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Thesis

May 7, 2014 by hgpu

Orchestrating Thread Scheduling and Cache Management to Improve Memory System Throughput in Throughput Processors

Dong Li

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Tags: Computer science, GPGPU-sim, Performance, Thesis

May 7, 2014 by hgpu

Coalition Structure Generation with the Graphics Processing Unit

Krzysztof Pawlowski, Karol Kurach, Kim Svensson, Sarvapali Ramchurn, Tomasz P. Michalak, Talal Rahwan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660, Package, Performance

May 3, 2014 by hgpu

A Performance Optimization Support Framework for GPU-based Traffic Simulations with Negotiating Agents

Yoshihito Sano, Yoshiaki Kadono, Naoki Fukuta

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Tags: Artificial intelligence, Computer science, nVidia, nVidia GeForce GT 320 M, OpenCL, Performance

May 3, 2014 by hgpu

Address Selection for Efficient Barriers on the Intel Xeon Phi

Romain Dolbeau

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Tags: Computer science, Intel Xeon Phi, Performance

May 2, 2014 by hgpu

Sparser, Better, Faster GPU Parsing

David Hall, Taylor Berg-Kirkpatrick, John Canny, Dan Klein

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Tags: Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Performance

April 21, 2014 by hgpu

Computational cost estimates for parallel shared memory isogeometric multi-frontal solvers

M.Wozniak, K.Kuznik, M. Paszynski, V. M. Calo, D. Pardo

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 780, Performance, Tesla C2070

April 21, 2014 by hgpu

Dynamic Instrumentation and Optimization for GPU Applications

Naila Farooqui, Christopher J. Rossbach, Yuan Yu

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla M2075

April 16, 2014 by hgpu

Test-driving Intel Xeon Phi

Jianbin Fang, Henk Sips, Lilun Zhang, Chuanfu Xu, Yonggang Che, Ana Lucia Varbanescu

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Tags: Computer science, Intel Xeon Phi, Package, Performance

April 13, 2014 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

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

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

Performance Analysis of Sobel Edge Filter on Heterogeneous System Using OpenCL

Dynamic Orchestration of Massively Data Parallel Execution

Fine-grain Task Aggregation and Coordination on GPUs

Improving the Programmability of GPU Architectures

Orchestrating Thread Scheduling and Cache Management to Improve Memory System Throughput in Throughput Processors

Coalition Structure Generation with the Graphics Processing Unit

A Performance Optimization Support Framework for GPU-based Traffic Simulations with Negotiating Agents

Address Selection for Efficient Barriers on the Intel Xeon Phi

Sparser, Better, Faster GPU Parsing

Computational cost estimates for parallel shared memory isogeometric multi-frontal solvers

Dynamic Instrumentation and Optimization for GPU Applications

Test-driving Intel Xeon Phi

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

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

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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