high performance computing on graphics processing units: hgpu.org

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Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Balint Pal Toth, Balint Czeba

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan X, Speech recognition

August 16, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

Near Memory Similarity Search on Automata Processors

Vincent T. Lee, Justin Kotalik, Carlo C. Del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin

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Tags: ARM, Computer science, CUDA, FPGA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, nVidia Jetson TK1

August 16, 2016 by hgpu

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

Gopalakrishna Hegde, Siddhartha, Nachiappan Ramasamy, Nachiket Kapre

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Tags: Computer science, CUDA, Deep learning, DSP, FPGA, nVidia, nVidia Tegra TX1

August 11, 2016 by hgpu

A Comparison of Potential Interfaces for Batched BLAS Computations

Samuel D. Relton, Pedro Valero-Lara, Mawussi Zounon

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Tags: BLAS, Computer science, CUDA, Linear Algbera, nVidia, Package, Tesla K40

August 11, 2016 by hgpu

Parallel LDPC Decoding on a Heterogeneous Platform using OpenCL

Jung-Hyun Hong, Joo-Yul Park, Ki-Seok Chung

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Tags: Computer science, Error recovery, Heterogeneous systems, nVidia, nVidia GeForce GTX 860 M, OpenCL

August 11, 2016 by hgpu

Accelerating Computational Finance Simulations with OpenCL

Michail Papadimitriou, Joris Cramwinckel, Ana Lucia Varbanescu

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Tags: Computational finance, Finance, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan, OpenCL, OpenMP, performance portability

August 8, 2016 by hgpu

Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

Kevin L. Keys, Gary K. Chen, Kenneth Lange

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Tags: Bioinformatics, Genetics, Machine learning, nVidia, OpenCL, Package, Statistics, Tesla C2050

August 8, 2016 by hgpu

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schluter, Hermann Ney

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Package, RNN, Speech recognition

August 4, 2016 by hgpu

A survey of sparse matrix-vector multiplication performance on large matrices

Max Grossman, Christopher Thiele, Mauricio Araya-Polo, Florian Frank, Faruk O. Alpak, Vivek Sarkar

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Tags: Computer science, CUDA, Linear Algebra, Numerical Analysis, nVidia, Performance, Sparse matrix, survey, Tesla M2070

August 4, 2016 by hgpu

A Gb/s Parallel Block-based Viterbi Decoder for Convolutional Codes on GPU

Hao Peng, Rongke Liu, Yi Hou, Ling Zhao

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 980, Signal processing

August 4, 2016 by hgpu

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Zhehuan Zhao, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

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Tags: Bioinformatics, Biology, CUDA, Deep learning, Neural networks, NLP, nVidia, Package, Python, Tesla K20, Text mining

August 1, 2016 by hgpu

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

MaxKernel: Intelligent TPU Kernel Engineering Agents

MaxKernel: Agentic Kernel Generation for TPUs

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high performance computing on graphics processing units: hgpu.org

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Learning Structured Sparsity in Deep Neural Networks

Near Memory Similarity Search on Automata Processors

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

A Comparison of Potential Interfaces for Batched BLAS Computations

Parallel LDPC Decoding on a Heterogeneous Platform using OpenCL

Accelerating Computational Finance Simulations with OpenCL

Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

A survey of sparse matrix-vector multiplication performance on large matrices

A Gb/s Parallel Block-based Viterbi Decoder for Convolutional Codes on GPU

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Recent source codes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

nvsass-disassembler

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

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