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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

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

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

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

Co-design of a particle-in-cell plasma simulation code for Intel Xeon Phi: a first look at Knights Landing

Igor Surmin, Sergey Bastrakov, Zakhar Matveev, Evgeny Efimenko, Arkady Gonoskov, Iosif Meyerov

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Tags: Computational Physics, Intel Xeon Phi, OpenMP, Particle-in-cell methods, Physics, Plasma physics

August 8, 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

A Comprehensive Performance Analysis of HSA and OpenCL 2.0

Saoni Mukherjee, Yifan Sun, Paul Blinzer, Amir Kavyan Ziabari, David Kaeli

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Tags: Algorithms, ATI, Benchmarking, Computer science, Heterogeneous systems, OpenCL, Performance

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

OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory

Maciej Wielgosz, Marcin Pietron

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Tags: Algorithms, AMD Radeon R9 390, ATI, Computer science, Computer vision, Machine learning, OpenCL, Package, PyOpenCL, Python

August 8, 2016 by hgpu

Daino: A High-level Framework for Parallel and Efficient AMR on GPUs

Mohamed Wahib, Naoya Maruyama, Takayuki Aoki

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Tags: Adaptive Mesh Refinement, CUDA

August 5, 2016 by wahibium

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Applications

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

A Comparison of Potential Interfaces for Batched BLAS Computations

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

Parallel LDPC Decoding on a Heterogeneous Platform using OpenCL

Co-design of a particle-in-cell plasma simulation code for Intel Xeon Phi: a first look at Knights Landing

Accelerating Computational Finance Simulations with OpenCL

A Comprehensive Performance Analysis of HSA and OpenCL 2.0

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

OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory

Daino: A High-level Framework for Parallel and Efficient AMR on GPUs

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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