high performance computing on graphics processing units: hgpu.org

hgpu.org » FPGA

HPP-Controller: An intra-node controller designed for connecting heterogeneous CPUs

Qiang Li, Panyong Zhang, Ninghui Sun

Tags: Cell processor, Computer science, FPGA, Heterogeneous systems, Memory, Performance

April 19, 2011 by hgpu

Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?

Eric S. Chung, Peter A. Milder, James C. Hoe, Ken Mai

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Tags: ASIC, ATI, ATI CAL, ATI Radeon HD 5870, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance

April 19, 2011 by hgpu

Parallel Approaches for SWAMP Sequence Alignment

Shannon Steinfadt, Kevin Schaffer

Tags: Bioinformatics, Biology, Cell processor, FPGA, nVidia, Sequence alignment, Smith-Waterman algorithm

April 19, 2011 by hgpu

A Comparative Study on ASIC, FPGAs, GPUs and General Purpose Processors in the O(N^2) Gravitational N-body Simulation

Tsuyoshi Hamada, Khaled Benkrid, Keigo Nitadori, Makoto Taiji

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Tags: Astrophysics, Cosmology, FPGA, N-body simulation, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GX2, Physics

April 8, 2011 by hgpu

Accelerating Phase Correlation Functions Using GPU and FPGA

Kentaro Matsuo, Tsuyoshi Hamada, Masayuki Miyoshi, Yuichiro Shibata, and Kiyoshi Oguri

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Tags: Computer science, CUDA, FFT, FPGA, Image registration, nVidia, nVidia GeForce 8800 GTS

April 7, 2011 by hgpu

MARC: A Many-Core Approach to Reconfigurable Computing

Ilia Lebedev, Shaoyi Cheng, Austin Doupnik, James Martin, Christopher Fletcher, Daniel Burke, Mingjie Lin, John Wawrzynek

Tags: Compilers, Computer science, FPGA, OpenCL, Performance

April 2, 2011 by hgpu

OpenCL-based design methodology for application-specific processors

Pekka O. Jaaskelainen, Carlos S. de La Lama, Pablo Huerta, Jarmo H. Takala

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Tags: AES, Computer science, FPGA, MPI, OpenCL, Security

March 30, 2011 by hgpu

Bridging the GPGPU-FPGA efficiency gap

Christopher W. Fletcher, Ilia A. Lebedev, Narges B. Asadi, Daniel R. Burke, John Wawrzynek

Tags: Algorithms, Bayesian, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL

March 22, 2011 by hgpu

Performance comparison of FPGA, GPU and CPU in image processing

Shuichi Asano, Tsutomu Maruyama, Yoshiki Yamaguchi

Tags: Filtering, FPGA, Image processing, Performance

March 21, 2011 by hgpu

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

David Barrie Thomas, Lee Howes, Wayne Luk

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Pseudo-random number generators

March 20, 2011 by hgpu

Mersenne Twister Random Number Generation on FPGA, CPU and GPU

Xiang Tian, Khaled Benkrid

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Pseudo-random number generators

March 20, 2011 by hgpu

FPGA vs. GPU for sparse matrix vector multiply

Yan Zhang, Yasser H. Shalabi, Rishabh Jain, Krishna K. Nagar, Jason D. Bakos

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Tags: Computer science, CUDA, FPGA, Linear Algebra, nVidia, nVidia GeForce GTX 260, Sparse matrix

March 18, 2011 by hgpu

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

See all packages

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high performance computing on graphics processing units: hgpu.org

HPP-Controller: An intra-node controller designed for connecting heterogeneous CPUs

Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?

Parallel Approaches for SWAMP Sequence Alignment

A Comparative Study on ASIC, FPGAs, GPUs and General Purpose Processors in the O(N^2) Gravitational N-body Simulation

Accelerating Phase Correlation Functions Using GPU and FPGA

MARC: A Many-Core Approach to Reconfigurable Computing

OpenCL-based design methodology for application-specific processors

Bridging the GPGPU-FPGA efficiency gap

Performance comparison of FPGA, GPU and CPU in image processing

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

Mersenne Twister Random Number Generation on FPGA, CPU and GPU

FPGA vs. GPU for sparse matrix vector multiply

Recent source codes

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

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