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hgpu.org » Applications » Computer science

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Peter Thoman, Klaus Kofler, Heiko Studt, John Thomson, Thomas Fahringer

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Benchmarking, Cell processor, Computer science, nVidia, nVidia GeForce GTX 275, nVidia GeForce GTX 460, OpenCL, Optimization, Tesla C2050

September 8, 2011 by hgpu

Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL

Leonid Djinevski, Igor Mishkovski, Dimitar Trajanov

Tags: Algorithms, Clustering, Computer science, Networks, OpenCL, OpenMP

September 8, 2011 by hgpu

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Vishakha Gupta, Karsten Schwan, Niraj Tolia, Vanish Talwar, Parthasarathy Ranganathan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX, Operating systems, Task scheduling, Virtualization

September 7, 2011 by hgpu

GPU-Based approaches for multiobjective local search algorithms. A case study: the flowshop scheduling problem

The Van Luong, Nouredine Melab, El-Ghazali Talbi

Tags: Algorithms, Computer science, Search

September 7, 2011 by hgpu

Automatic CPU-GPU communication management and optimization

Thomas B. Jablin, Prakash Prabhu, James A. Jablin, Nick P. Johnson, Stephen R. Beard, David I. August

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Programming techniques

September 7, 2011 by hgpu

MacroSS: macro-SIMDization of streaming applications

Amir H. Hormati, Yoonseo Choi, Mark Woh, Manjunath Kudlur, Rodric Rabbah, Trevor Mudge, Scott Mahlke

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Tags: Code generation, Compilers, Computer science, Optimization, Performance, Programming Languages

September 7, 2011 by hgpu

CUDA-level performance with python-level productivity for Gaussian mixture model applications

H. Cook, E. Gonina, S. Kamil, G. Friedland, D. Patterson, A. Fox

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, OpenCL, Python

September 7, 2011 by hgpu

Chameleon: Virtualizing idle acceleration cores of a heterogeneous multicore processor for caching and prefetching

Dong Hyuk Woo, Joshua B. Fryman, Allan D. Knies, Hsien-Hsin S. Lee

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Tags: Benchmarking, Computer science, Heterogeneous systems, Prefetch

September 7, 2011 by hgpu

Operating systems must support GPU abstractions

Christopher J. Rossbach, Jon Currey, Emmett Witchel

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Tags: Computer science, nVidia, nVidia GeForce GT 230, Operating systems

September 7, 2011 by hgpu

A code-based analytical approach for using separate device coprocessors in computing systems

Volker Hampel, Grigori Goronzy, Erik Maehle

Tags: Computer science, FPGA, Performance

September 7, 2011 by hgpu

GPU-based asynchronous particle swarm optimization

Luca Mussi, Youssef S.G. Nashed, Stefano Cagnoni

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, Optimization, Performance

September 7, 2011 by hgpu

The future of microprocessors

Shekhar Borkar, Andrew A. Chien

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Tags: Computer science, Energy-efficient computing, Hardware Architecture, Review

September 7, 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

Applications

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Accelerating Clustering Coefficient Calculations on a GPU Using OPENCL

Pegasus: coordinated scheduling for virtualized accelerator-based systems

GPU-Based approaches for multiobjective local search algorithms. A case study: the flowshop scheduling problem

Automatic CPU-GPU communication management and optimization

MacroSS: macro-SIMDization of streaming applications

CUDA-level performance with python-level productivity for Gaussian mixture model applications

Chameleon: Virtualizing idle acceleration cores of a heterogeneous multicore processor for caching and prefetching

Operating systems must support GPU abstractions

A code-based analytical approach for using separate device coprocessors in computing systems

GPU-based asynchronous particle swarm optimization

The future of microprocessors

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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