high performance computing on graphics processing units: hgpu.org

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SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

Farouk Mansouri, Sylvain Huet, Dominique Houzet

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Tags: Algorithms, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GT 610, nVidia GeForce GTX Titan, nVidia Quadro 4000, Signal processing

December 20, 2014 by hgpu

FPGA: An Efficient And Promising Platform For Real-Time Image Processing Applications

Sparsh Mittal, Saket Gupta, Sudeb Dasgupta

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Tags: DSP, FPGA, Image processing, survey

November 19, 2014 by sparsh0mittal

Hierarchical Mapping Techniques for Signal Processing Systems on Parallel Platforms

Lai-Huei Wang

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Tags: Computer vision, CUDA, Design space exploration, DSP, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 680, Signal processing, Thesis

October 31, 2014 by hgpu

An OpenCL Runtime and Scheduler for Embedded Multicore DSP Parallel Systems

Li Tian, Fugen Zhou, Cai Meng

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Tags: Computer science, DSP, Embedded high-performance computing, nVidia, OpenCL, Task scheduling

May 18, 2014 by hgpu

A two-level task scheduler on Multiple DSP system for OpenCL

Li Tian, Cai Meng, Fugen Zhou

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Tags: Computer science, DSP, nVidia, OpenCL, Task scheduling

April 9, 2014 by hgpu

Task migration of DSP application specified with a DFG and implemented with the BSP computing model on a CPU-GPU cluster

Farouk Mansouri, Sylvain Huet, Vincent Fristot, Dominique Houzet

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Tags: Computer science, CUDA, DSP, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia Quadro 4000, Signal processing

February 2, 2014 by hgpu

Implementations of the Hough Transform on the Embedded Multicore Processors

Xin Zhou, Norihiro Tomagou, Yasuaki Ito, Koji Nakano

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Tags: CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 680

January 11, 2014 by hgpu

Multi-Scale Scheduling Techniques for Signal Processing Systems

Zheng Zhou

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Tags: Algorithms, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Particle swarm optimization, Signal processing, Thesis

October 19, 2013 by hgpu

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Egil Fykse

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Tags: Benchmarking, Computer science, Computer vision, CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 660 Ti, nVidia Quadro 1000 M, OpenCV, Performance, Thesis

October 15, 2013 by hgpu

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

George Zaki

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Tags: Algorithms, Code generation, Computational Engineering, CUDA, Data parallelism, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, Signal processing, Thesis

July 12, 2013 by hgpu

Comparative study of parallel programming models for multicore computing

Akhtar Ali

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Tags: Benchmarking, Computer science, DSP, Heterogeneous systems, nVidia, OpenCL, OpenMP, Tesla M2050, Thesis

July 7, 2013 by hgpu

Implementations of the FFT algorithm on GPU

Sreehari Ambuluri

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Tags: Algorithms, CUDA, DSP, FFT, nVidia, nVidia GeForce GTX 560, Signal processing, Thesis

May 4, 2013 by hgpu

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BLOCKBENCH: A Framework for Analyzing Private Blockchains

Blockchain Goes Green? Part II: Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge

hls4ml: Machine learning on FPGAs using HLS

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

GPU-based JSON data processing using structural indexes

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Analytical Performance Estimation during Code Generation on Modern GPUs

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

The Celerity High-level API: C++20 for Accelerator Clusters

GPU-Sched

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

FPGA: An Efficient And Promising Platform For Real-Time Image Processing Applications

Hierarchical Mapping Techniques for Signal Processing Systems on Parallel Platforms

An OpenCL Runtime and Scheduler for Embedded Multicore DSP Parallel Systems

A two-level task scheduler on Multiple DSP system for OpenCL

Task migration of DSP application specified with a DFG and implemented with the BSP computing model on a CPU-GPU cluster

Implementations of the Hough Transform on the Embedded Multicore Processors

Multi-Scale Scheduling Techniques for Signal Processing Systems

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Scalable Techniques for Scheduling and Mapping DSP Applications onto Embedded Multiprocessor Platforms

Comparative study of parallel programming models for multicore computing

Implementations of the FFT algorithm on GPU

Recent source codes

BLOCKBENCH: A Framework for Analyzing Private Blockchains

hls4ml: Machine learning on FPGAs using HLS

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

GPU-Sched

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