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On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

Konstantinos Krommydas, Wu-chun Feng, Muhsen Owaida, Christos D. Antonopoulos, Nikolaos Bellas

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Tags: APU, ATI, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, OpenCL, Package, Performance

June 26, 2014 by hgpu

Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels

Sreepathi Pai, R. Govindarajan, Matthew J. Thazhuthaveetil

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Tags: Computer science, CUDA, nVidia, Operating systems, Performance, Tesla K20

June 25, 2014 by hgpu

The Power-Performance Tradeoffs of the Intel Xeon Phi on HPC Applications

Bo Li, Hung-Ching Chang, Shuaiwen Leon Song, Chun-Yi Su, Timmy Meyer, John Mooring, Kirk Cameron

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, nVidia, Performance, Tesla C2050

June 22, 2014 by hgpu

A Case Against Small Data Types on GPGPUs

Ahmad Lashgar, Amirali Baniasadi

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Performance

June 18, 2014 by hgpu

An in-depth performance analysis of irregular workloads on VLIW APU

Matthew Doerksen

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Tags: APU, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, OpenCL, Performance, Thesis

June 16, 2014 by hgpu

Dynamic loop vectorization for executing OpenCL kernels on CPUs

Izzat El Hajj

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Tags: Computer science, Heterogeneous systems, OpenCL, Performance, Thesis

June 14, 2014 by hgpu

Performance models for CPU-GPU data transfers

B. van Werkhoven, J. Maassen, F.J. Seinstra, H.E. Bal

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Tags: Computer science, CUDA, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, PCIe, Performance, Performance prediction, Tesla K20

June 5, 2014 by bennotsi

An Extensible Component-based Approach to Simulation Systems on Heterogeneous Clusters

Roger Viet Hoang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 570, Performance, Tesla C2050, Thesis, Virtual reality

May 23, 2014 by hgpu

Developing a compiler for the XeonPhi

Paul Cockshott, Susanne Oehler, Tian Xu

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Tags: Compilers, Computer science, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX 770, Performance

May 21, 2014 by hgpu

Real-time Image Processing on Low Cost Embedded Computers

Sunil Shah

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Tags: ARM, Computer vision, Image processing, OpenCV, Package, Performance, Thesis

May 15, 2014 by hgpu

Impact of Modern OpenGL on FPS

Jan Cejka

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Tags: 3D Graphics and Realism, Computer science, nVidia, nVidia GeForce GTX 580, nVidia Quadro 6000, OpenGL, Performance, Rendering

May 13, 2014 by hgpu

Deriving Shape Grammars on the GPU

Mark Dokter

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan, OpenGL, Performance

May 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

On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels

The Power-Performance Tradeoffs of the Intel Xeon Phi on HPC Applications

A Case Against Small Data Types on GPGPUs

An in-depth performance analysis of irregular workloads on VLIW APU

Dynamic loop vectorization for executing OpenCL kernels on CPUs

Performance models for CPU-GPU data transfers

An Extensible Component-based Approach to Simulation Systems on Heterogeneous Clusters

Developing a compiler for the XeonPhi

Real-time Image Processing on Low Cost Embedded Computers

Impact of Modern OpenGL on FPS

Deriving Shape Grammars 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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