high performance computing on graphics processing units: hgpu.org

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Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method

Aparna Chandramowlishwarany, Kamesh Madduri, Richard Vuduc

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Tags: Computer science, Fast multipole method, Optimization, Performance

January 11, 2011 by hgpu

High-performance CUDA kernel execution on FPGAs

Alexandros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen Mei

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Tags: Computer science, CUDA, FPGA, Performance, Programming techniques

January 11, 2011 by hgpu

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Rick Weber, Akila Gothandaraman, Robert J. Hinde, Gregory D. Peterson

Tags: ATI, Brook, Computer science, CUDA, FPGA, nVidia, OpenCL, Performance

January 10, 2011 by hgpu

Self-Configuring Applications for Heterogeneous Systems: Program Composition and Optimization Using Cognitive Techniques

M. W. Hall, Y. Gil, R. F. Lucas

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Tags: Compilers, Computer science, FPGA, nVidia, Optimization, Performance

January 8, 2011 by hgpu

FPGA-based acceleration of CHARMM-potential minimization

Bharat Sukhwani, Martin C. Herbordt

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Tags: Chemistry, FPGA, Molecular modeling, nVidia, Performance, Tesla C1060

January 3, 2011 by hgpu

Using Reconfigurable Logic to Optimise GPU Memory Accesses

Ben Cope, Peter Y. K. Cheung, Wayne Luk

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Tags: Computer science, Memory model, nVidia, nVidia GeForce 7900 GTX, Performance

December 27, 2010 by hgpu

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Veerendra Allada, Troy Benjegerdes, Brett Bode

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Presentation, Tesla S1070, Tesla T10

December 27, 2010 by hgpu

Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors

Nachiket Kapre, Andre DeHon

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Tags: Cell processor, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 9600 GT, Performance

December 26, 2010 by hgpu

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

Frederico Pratas, Pedro Trancoso, Alexandros Stama

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Tags: Cell processor, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Performance, Programming techniques

December 24, 2010 by hgpu

Pushing the Envelope: Extreme Network Coding on the GPU

Hassan Shojania, Baochun Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques

December 22, 2010 by hgpu

Demystifying GPU microarchitecture through microbenchmarking

Henry Wong, Misel-Myrto Papadopoulou, Maryam Sadooghi-Alvandi, Andreas Moshovos

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Tags: Architecture, Computer science, nVidia, nVidia GeForce GTX 280, Performance

December 21, 2010 by hgpu

Shader Performance Analysis on a Modern GPU Architecture

Victor Moya, Carlos Gonzalez, Jordi Roca, Agustin Fernandez, Roger Espasa

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Tags: 3D Graphics and Realism, ATI, Computer science, OpenGL, Performance, Rendering

December 18, 2010 by hgpu

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

See all packages

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high performance computing on graphics processing units: hgpu.org

Diagnosis, Tuning, and Redesign for Multicore Performance: A Case Study of the Fast Multipole Method

High-performance CUDA kernel execution on FPGAs

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Self-Configuring Applications for Heterogeneous Systems: Program Composition and Optimization Using Cognitive Techniques

FPGA-based acceleration of CHARMM-potential minimization

Using Reconfigurable Logic to Optimise GPU Memory Accesses

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Performance comparison of single-precision SPICE Model-Evaluation on FPGA, GPU, Cell, and multi-core processors

Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

Pushing the Envelope: Extreme Network Coding on the GPU

Demystifying GPU microarchitecture through microbenchmarking

Shader Performance Analysis on a Modern GPU Architecture

Recent source codes

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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