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Three storage formats for sparse matrices on GPGPUs

Davide Barbieri, Valeria Cardellini, Alessandro Fanfarillo, Salvatore Filippone

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 660, Package, Sparse matrix, Tesla M2070

September 9, 2015 by hgpu

Experimentation Procedure for Offloaded Mini-Apps Executed on Cluster Architectures with Xeon Phi Accelerators

Gary Lawson, Vaibhav Sundriyal, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, Molecular dynamics

September 9, 2015 by hgpu

Dissecting GPU Memory Hierarchy through Microbenchmarking

Xinxin Mei, Xiaowen Chu

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Tags: Benchmarking, cache, Computer science, CUDA, Hardware Architecture, nVidia, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 780, nVidia GeForce GTX 980, Package

September 9, 2015 by hgpu

Accelerating Multiple Compound Comparison Using LINGO-based Load-Balancing Strategies on Multi-GPUs

Chun-Yuan Lin, Chung-Hung Wang, Che-Lun Hung, Yu-Shiang Lin

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Tags: Computer science, CUDA, load balancing, nVidia, OpenMP, Tesla K20

September 8, 2015 by hgpu

Assessing the hardness of SVP algorithms in the presence of CPUs and GPUs

Fabio Jose Goncalves Correia

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, Quantum computing, Security, Thesis, Voronoi diagram

September 8, 2015 by hgpu

Contributions to the Efficient Use of General Purpose Coprocessors: Kernel Density Estimation as Case Study

Unai Lopez Novoa

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Tags: Algorithms, ATI, ATI Radeon HD 6950, Computational Complexity, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 650, OpenCL, Thesis

September 8, 2015 by hgpu

Accelerating Web Search using GPUs

Rimon Tadros

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Tags: C++ AMP, Computer science, nVidia, nVidia GeForce GTX 660 Ti, nVidia GeForce GTX 750 Ti, Search, Thesis

September 8, 2015 by hgpu

A Survey Of Architectural Techniques for Near-Threshold Computing

Sparsh Mittal

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Tags: cpu, Energy-efficient computing, low voltage computing, Memory, Power-efficient computing, survey

September 7, 2015 by sparsh0mittal

Waste Not, Want Not! Managing relational data in asymmetric memories

Holger Pirk

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Tags: ATI, ATI Radeon HD 5850, Computer science, Databases, nVidia, nVidia GeForce GTX 480, OpenCL, Thesis

September 5, 2015 by hgpu

Virtualizing Data Parallel Systems for Portability, Productivity, and Performance

Janghaeng Lee

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Tags: Computer science, LLVM, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 760, OpenCL, Thesis, Virtualization

September 5, 2015 by hgpu

Parallel Execution of the ASP Computation – an Investigation on GPUs

Agostino Dovier, Andrea Formisano, Enrico Pontelli, Flavio Vella

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce GT 520, nVidia GeForce GT 640, nVidia GeForce GTX Titan X, Search strategies, Tesla C2075, Tesla K20, Tesla K80

September 5, 2015 by hgpu

Convolutional Neural Network for Sentence Classification

Yahui Chen

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Tags: CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, NLP, nVidia, nVidia GeForce GTX Titan X, Thesis

September 5, 2015 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Applications

Three storage formats for sparse matrices on GPGPUs

Experimentation Procedure for Offloaded Mini-Apps Executed on Cluster Architectures with Xeon Phi Accelerators

Dissecting GPU Memory Hierarchy through Microbenchmarking

Accelerating Multiple Compound Comparison Using LINGO-based Load-Balancing Strategies on Multi-GPUs

Assessing the hardness of SVP algorithms in the presence of CPUs and GPUs

Contributions to the Efficient Use of General Purpose Coprocessors: Kernel Density Estimation as Case Study

Accelerating Web Search using GPUs

A Survey Of Architectural Techniques for Near-Threshold Computing

Waste Not, Want Not! Managing relational data in asymmetric memories

Virtualizing Data Parallel Systems for Portability, Productivity, and Performance

Parallel Execution of the ASP Computation – an Investigation on GPUs

Convolutional Neural Network for Sentence Classification

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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