high performance computing on graphics processing units: hgpu.org

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File I/O on Intel Xeon Phi Coprocessors: RAM disks, VirtIO, NFS and Lustre

Andrey Vladimirov, Vadim Karpusenko, Tony Yoo

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Tags: Benchmarking, Computer science, I/O, Intel Xeon Phi, Portability, Xeon Phi

July 29, 2014 by Andrey Vladimirov

ADHA: Automatic Data layout framework for Heterogeneous Architectures

Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar

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Tags: Benchmarking, Compilers, Computer science, Heterogeneous systems, nVidia, OpenCL, Programming Languages, Tesla M2050

July 24, 2014 by hgpu

Multi-Core Programming Design Patterns: Stream Processing Algorithms for Dynamic Scene Perceptions

Kannappan Palaniappan

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Tags: Algorithms, Benchmarking, Cell processor, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 460, Tesla C2070

July 24, 2014 by hgpu

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

David Rohr

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Review, Thesis

July 14, 2014 by hgpu

Benchmarking the Memory Hierarchy of Modern GPUs

Xinxin Mei, Kaiyong Zhao, Chengjian Liu, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 780, Package, Performance

July 14, 2014 by hgpu

Performance and Efficiency Analysis of Modern Accelerators: Fine-Grained Parallelism on the Intel Xeon Phi

Robrecht Dewaele

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Package, Performance, Thesis

June 28, 2014 by hgpu

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

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Faiz Khan, Vincent Foley-Bourgon, Sujay Kathrotia, Erick Lavoie, Laurie Hendren

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Java, nVidia, OpenCL, Package, Tesla C2050

June 23, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

High Performance Non-Blocking Collective Communication for Next Generation Infiniband Clusters

Krishna Kandalla

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Tags: Benchmarking, Computer science, InfiniBand, Intel Xeon Phi, Linear Algebra, MPI

March 14, 2014 by hgpu

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Seyong Lee, Dong Li, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenACC, Performance, Tesla M2090

March 7, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 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

File I/O on Intel Xeon Phi Coprocessors: RAM disks, VirtIO, NFS and Lustre

ADHA: Automatic Data layout framework for Heterogeneous Architectures

Multi-Core Programming Design Patterns: Stream Processing Algorithms for Dynamic Scene Perceptions

On Development, Feasibility, and Limits of Highly Efficient CPU and GPU Programs in Several Fields

Benchmarking the Memory Hierarchy of Modern GPUs

Performance and Efficiency Analysis of Modern Accelerators: Fine-Grained Parallelism on the Intel Xeon Phi

On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

Using JavaScript and WebCL for Numerical Computations: A Comparative Study of Native and Web Technologies

Parallel Support Vector Machines in Practice

High Performance Non-Blocking Collective Communication for Next Generation Infiniband Clusters

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

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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