high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

An Auto-tuning Solution to Data Streams Clustering in OpenCL

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: ATI, ATI Radeon HD 5870, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Optimization, Performance

October 3, 2011 by hgpu

Power Management and Optimization

Hari Sundararajan

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Tags: Computer science, CUDA, Energy-efficient computing, Green, nVidia, nVidia GeForce GTX 260, Performance, Thesis

October 2, 2011 by hgpu

Power-Efficient Accelerators for High-Performance Applications

Ganesh Suryanarayan Dasika

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Tags: Computer science, Energy-efficient computing, Performance, Thesis

October 2, 2011 by hgpu

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

Christopher J. Rossbach, Jon Currey, Mark Silberstein, Baishakhi Ray, Emmett Witchel

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Tags: Computer science, CUDA, HLSL, nVidia, nVidia GeForce GT 230, nVidia GeForce GTX 470, nVidia GeForce GTX 580, OpenCL, Operating systems, Performance, Programming techniques

October 2, 2011 by hgpu

A Complete Descritpion of the UnPython and Jit4GPU Framework

Rahul Garg, Jose Nelson Amaral

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5850, ATI Stream, Compilers, Computer science, OpenMP, Optimization, Performance, Python

October 2, 2011 by hgpu

Customizable Domain-Specific Computing

Jason Cong, Glenn Reinman, Alex Bui, Vivek Sarkar

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Tags: Computer science, FPGA, nVidia, Performance, Programming techniques, Tesla C1060

October 1, 2011 by hgpu

A Comprehensive Performance Comparison of CUDA and OpenCL

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Cell processor, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Optimization, Performance

October 1, 2011 by hgpu

Accelerating Vector Calculations on GPU

Karol Grondzak, Penka Martincova, Ondrej Such

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Tesla C1060, Tutorial

October 1, 2011 by hgpu

Exploring The Latency and Bandwidth Tolerance of CUDA Applications

Gregory Diamos, Sudnya Padalikar

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, Memory, nVidia, Performance, PTX, Tesla C1060

September 30, 2011 by hgpu

Stack-less SIMT reconvergence at low cost

Sylvain Collange

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Tags: ATI, Computer science, Hardware Architecture, Memory, nVidia, Performance

September 30, 2011 by hgpu

FATSEA-An Architectural Simulator for General Purpose Computing on GPUs

K.E. Ostby, J.L. Aragon, J.M. Garcia, M. Ujaldon

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Tags: Compilers, Computer science, CUDA, nVidia, Performance, PTX, Tesla C870

September 30, 2011 by hgpu

Adding special-purpose processor support to the Erlang VM

Christofer Ferm

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Tags: Computer science, DSP, nVidia, nVidia GeForce 9400 M, OpenCL, Performance, Thesis, Virtualization

September 30, 2011 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

An Auto-tuning Solution to Data Streams Clustering in OpenCL

Power Management and Optimization

Power-Efficient Accelerators for High-Performance Applications

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

A Complete Descritpion of the UnPython and Jit4GPU Framework

Customizable Domain-Specific Computing

A Comprehensive Performance Comparison of CUDA and OpenCL

Accelerating Vector Calculations on GPU

Exploring The Latency and Bandwidth Tolerance of CUDA Applications

Stack-less SIMT reconvergence at low cost

FATSEA-An Architectural Simulator for General Purpose Computing on GPUs

Adding special-purpose processor support to the Erlang VM

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++

Most viewed papers (last 30 days)