high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Jonathan Passerat-Palmbach, Jonathan Caux, Pridi Siregar, Claude Mazel, D.R.C.Hill

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Tags: Benchmarking, Computer science, CUDA, nVidia, Stochastic simulation, Tesla C2050, Warp level parallelism

December 21, 2011 by hgpu

Analysis of GPGPU Platforms Efficiency in General-Purpose Computations

Pavel Kartashev, Vladislavs Nazaruk

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Tags: ATI, ATI Radeon HD 5750, Benchmarking, Computer science, nVidia, nVidia GeForce GT 240, OpenCL, Performance

December 20, 2011 by hgpu

Towards Automatic C Programs Optimization and Parallelization using the PIPS-PoCC Integration

Dounia Khaldi, Corinne Ancourt, Francois Irigoin

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, MPI, nVidia, OpenMP, Optimization, Package

December 19, 2011 by hgpu

A Translation Framework for Executing the Sequential Binary Code on CPU/GPU Based Architectures

Erzhou Zhu, Haibing Guan, Guoxing Dong, Yindong Yang, Hongbo Yang

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, PTX

December 18, 2011 by hgpu

A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

Michael J. Anderson, David Sheffield, Kurt Keutzer

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia Quadro FX 6000, Package, Signal processing

December 16, 2011 by hgpu

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Uzi Vishkin, Rajeev Barua

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Tags: Algorithms, Benchmarking, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Prefetch, Rendering, Thesis

December 16, 2011 by hgpu

Affine Vector Cache for memory bandwidth savings

Sylvain Collange, Alexandre Kouyoumdjian

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Tags: Benchmarking, Computer science, CUDA, Memory model, nVidia, Tesla

December 16, 2011 by hgpu

Memory-level and Thread-level Parallelism Aware GPU Architecture Performance Analytical Model

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Benchmarking, Computer science, CUDA, Memory level parallelism, nVidia, nVidia Quadro FX 5600, Warp level parallelism

December 15, 2011 by hgpu

Performance in GPU Architectures: Potentials and Distances

Ahmad Lashgar, Amirali Baniasadi

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 5800, Performance

December 15, 2011 by hgpu

Graph Generation on GPUs using Dynamic Memory Allocation

A. Leist, K.A. Hawick

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Graph theory, Memory model, nVidia, nVidia GeForce GTX 580, Performance

December 14, 2011 by hgpu

Fast Neural Network Training on General Purpose Computers

Harshit Kharbanda, Roy H. Campbell

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia Quadro FX 1800

December 14, 2011 by hgpu

GPU Programming in a High Level Language: Compiling X10 to CUDA

Dave Cunningham, Rajesh Bordawekar, Vijay Saraswat

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, nVidia Quadro FX 3700 M, OpenCL, Tesla C1060, Tesla S1070

December 12, 2011 by hgpu

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

CodegenBench

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

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

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

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

Agentic Code Optimization via Compiler-LLM Cooperation

See all packages

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high performance computing on graphics processing units: hgpu.org

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Analysis of GPGPU Platforms Efficiency in General-Purpose Computations

Towards Automatic C Programs Optimization and Parallelization using the PIPS-PoCC Integration

A Translation Framework for Executing the Sequential Binary Code on CPU/GPU Based Architectures

A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Affine Vector Cache for memory bandwidth savings

Memory-level and Thread-level Parallelism Aware GPU Architecture Performance Analytical Model

Performance in GPU Architectures: Potentials and Distances

Graph Generation on GPUs using Dynamic Memory Allocation

Fast Neural Network Training on General Purpose Computers

GPU Programming in a High Level Language: Compiling X10 to CUDA

Recent source codes

CUDAnalyst (CUDA + Analyst)

CodegenBench

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

Device Virtual Machine (DVM)

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

Agentic Code Optimization via Compiler-LLM Cooperation

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