high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures

Jin Wangy, Norman Rubinz, Haicheng Wuy, Sudhakar Yalamanchiliy

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Tags: Algorithms, ATI, ATI Radeon HD 7660, ATI Radeon HD 7950, Computer science, Heterogeneous systems, OpenCL, Performance, Vehicular traffic

March 5, 2013 by hgpu

Performance Analysis of a Symmetric Cryptographic Algorithm on Multicore Architectures

Adrian Pousa, Victoria Sanz, Armando de Giusti

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Tags: AES, Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 560 Ti, Performance, Security

March 5, 2013 by hgpu

Future of GPGPU Micro-Architectural Parameters

Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal

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Tags: Computer science, Overview, Performance

February 25, 2013 by hgpu

Can PCM Benefit GPU? Reconciling Hybrid Memory Design with GPU Massive Parallelism for Energy Efficiency

Bin Wang, Bo Wu, Dong Li, Xipeng Shen, Weikuan Yu, Yizheng Jiao, Jeffrey Vetter

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

February 23, 2013 by hgpu

Performance Upper Bound Analysis and Optimization of SGEMM on Fermi and Kepler GPUs

Junjie Lai, Andre Seznec

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 680, Optimization, Performance

February 22, 2013 by hgpu

Developing Performance-Portable Molecular Dynamics Kernels in OpenCL

S. J. Pennycook, S. A. Jarvis

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Source codes

Tags: ATI, ATI FirePro V7800, ATI Radeon HD 6550, Benchmarking, Computer science, Molecular dynamics, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Performance, Tesla C1060, Tesla C2050

February 14, 2013 by hgpu

The Dual-Path Execution Model for Efficient GPU Control Flow

Minsoo Rhu, Mattan Erez

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, Performance

February 14, 2013 by hgpu

Document Stream Clustering using GPUs

Michael J. Szaszy, Hanan Samet

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Tags: Algorithms, Clustering, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 480, Performance

February 9, 2013 by hgpu

Effectiveness of program transformations and compilers for directive-based GPU programming models

Swapnil Avinash Ghike

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Optimization, Performance, Tesla X2090, Thesis

February 9, 2013 by hgpu

Using Hybrid Shared and Distributed Caching for Mixed-Coherency GPU Workloads

Nasser Anssari

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Tags: Computer science, CUDA, nVidia, Performance, Thesis

February 9, 2013 by hgpu

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

Erik Saule, Kamer Kaya, Umit V. Catalyurek

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Tags: Computer science, CUDA, Hardware Architecture, Intel, Intel Phi, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla C2050, Tesla K20

February 6, 2013 by hgpu

An Implementation of Conflict-Free Offline Permutation on the GPU

Akihiko Kasagi, Koji Nakano, Yasuaki Ito

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Tags: Computer science, CUDA, Memory, Memory model, nVidia, nVidia GeForce GTX 680, Performance

February 6, 2013 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

Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures

Performance Analysis of a Symmetric Cryptographic Algorithm on Multicore Architectures

Future of GPGPU Micro-Architectural Parameters

Can PCM Benefit GPU? Reconciling Hybrid Memory Design with GPU Massive Parallelism for Energy Efficiency

Performance Upper Bound Analysis and Optimization of SGEMM on Fermi and Kepler GPUs

Developing Performance-Portable Molecular Dynamics Kernels in OpenCL

The Dual-Path Execution Model for Efficient GPU Control Flow

Document Stream Clustering using GPUs

Effectiveness of program transformations and compilers for directive-based GPU programming models

Using Hybrid Shared and Distributed Caching for Mixed-Coherency GPU Workloads

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

An Implementation of Conflict-Free Offline Permutation on the GPU

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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