high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

GPU-accelerated HMM for Speech Recognition

Leiming Yu, Yash Ukidave, David Kaeli

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Package, Speech recognition

February 10, 2015 by hgpu

Analysis and Modeling of the Timing Behavior of GPU Architectures

Petros Voudouris

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, Thesis

February 10, 2015 by hgpu

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Michel Steuwer, Christian Fensch, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Programming Languages

February 10, 2015 by hgpu

A Survey of Architectural Techniques For DRAM Power Management

Sparsh Mittal

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Tags: Architecture, DVFS, Energy-efficient computing, HPC, main memory, Power-efficient computing

February 9, 2015 by sparsh0mittal

FIR filtering and AES encryption with OpenCL 2.0

Carter McCardwell, Tuan Dao, Saoni Mukherjee, David Kaeli

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Tags: AES, Computer science, Filtering, Heterogeneous systems, OpenCL, Security

February 9, 2015 by hgpu

Speech Recognition on Modern Graphic Processing Units

Leiming Yu, John Magrath, Ajey Pandey, Matthew Sears, David Kaeli

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Speech recognition

February 9, 2015 by hgpu

Fast Subgraph Matching on Large Graphs using Graphics Processors

Ha-Nguyen Tran, Jung-jae Kim, Bingsheng He

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Tesla C2050

February 9, 2015 by hgpu

Sparse Matrix-Vector Multiplication on GPU

Arash Ashari

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan, Sparse matrix, Tesla K10, Thesis

February 9, 2015 by hgpu

Fine-Tuning Vectorization and Memory Traffic on Intel Xeon Phi Coprocessors: LU Decomposition of Small Matrices

Andrey Vladimirov

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Tags: cache, Intel Xeon Phi, Ivy Bridge, Package

February 9, 2015 by Andrey Vladimirov

Power Management Techniques for Data Centers: A Survey

Sparsh Mittal

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Tags: Architecture, big data, cpu, Energy-efficient computing, HPC, Power-efficient computing

February 8, 2015 by sparsh0mittal

Extending the Gotran framework: LATEX and GPU acceleration

George Bahijevitch Bahij

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Tags: Computational modelling, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX Titan, ODEs, Ordinary differential equations, Python, Thesis

February 6, 2015 by hgpu

Unlocking Bandwidth for GPUs in CC-NUMA Systems

Neha Agarwal, David Nellans, Mike O'Connor, Stephen W. Keckler, Thomas F. Wenisch

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Prefetch

February 6, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

GPU-accelerated HMM for Speech Recognition

Analysis and Modeling of the Timing Behavior of GPU Architectures

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

A Survey of Architectural Techniques For DRAM Power Management

FIR filtering and AES encryption with OpenCL 2.0

Speech Recognition on Modern Graphic Processing Units

Fast Subgraph Matching on Large Graphs using Graphics Processors

Sparse Matrix-Vector Multiplication on GPU

Fine-Tuning Vectorization and Memory Traffic on Intel Xeon Phi Coprocessors: LU Decomposition of Small Matrices

Power Management Techniques for Data Centers: A Survey

Extending the Gotran framework: LATEX and GPU acceleration

Unlocking Bandwidth for GPUs in CC-NUMA Systems

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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