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Accelerated cryo-EM structure determination with parallelisation using GPUs in relion-2

Dari Kimanius, Bjorn O Forsberg, Sjors HW Scheres, Erik Lindahl

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Tags: Biology, Cryo-EM, CUDA, Image processing, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980 Ti

July 5, 2016 by hgpu

A Test Drive of the NVIDIA Jetson TX1 Developer Kit for Deep Learning and Computer Vision Applications

BDTI staff

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Tags: Algorithms, Caffe, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, OpenCV

July 5, 2016 by hgpu

Compiler-Assisted Workload Consolidation For Efficient Dynamic Parallelism on GPU

Hancheng Wu, Da Li, Michela Becchi

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Tesla K20

June 30, 2016 by hgpu

DeepBE: Learning Deep Binary Encoding for Multi-Label Classification

Chenghua Li, Qi Kang, Guojing Ge, Qiang Song, Hanqing Lu, Jian Cheng

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Tags: Computer science, Computer vision, CUDA, Deep learning, Image recognition, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X

June 30, 2016 by hgpu

Modified Levels of Parallel Odd-Even Transposition Sorting Network (OETSN) with GPU Computing using CUDA

Neetu Faujdar, SP Ghrera

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Sorting

June 30, 2016 by hgpu

Persistent RNNs: Stashing Recurrent Weights On-Chip

Gregory Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh

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Tags: Computer science, CUDA, Deep learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Speech recognition

June 30, 2016 by hgpu

Parallel and Distributed Deep Learning

Vishakh Hegde, Sheema Usmani

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Tags: Caffe, CNN, Computer science, CUDA, Deep learning, nVidia, nVidia GRID K520

June 28, 2016 by hgpu

A Synchronization-Free Algorithm for Parallel Sparse Triangular Solves

Weifeng Liu, Ang Li, Jonathan Hogg, Iain S. Duff, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 Fury X, ATI, Computer science, CUDA, Linear Algebra, nVidia GeForce GTX Titan X, OpenCL, Package, Sparse direct solvers, Tesla K40

June 28, 2016 by hgpu

Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks

Daniel Chavez

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Tags: Computer science, CUDA, nVidia, nVidia Quadro 1000 M, OpenCL, Pthreads, Thesis

June 28, 2016 by hgpu

GPU Based Real-Time Welding Simulation with Smoothed-Particle Hydrodynamics

Qing Gu

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Tags: cfd, CUDA, Fluid dynamics, nVidia, OpenGL, Physics, Thesis

June 28, 2016 by hgpu

Efficient and High-quality Sparse Graph Coloring on the GPU

Xuhao Chen, Pingfan Li, Canqun Yang

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Tags: Algorithms, Computer science, CUDA, nVidia, Package, Tesla K40

June 22, 2016 by hgpu

Tensor Contractions with Extended BLAS Kernels on CPU and GPU

Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka

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Tags: Algorithms, BLAS, Computer science, CUDA, FFT, Linear Algebra, nVidia, Tesla K40

June 22, 2016 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Accelerated cryo-EM structure determination with parallelisation using GPUs in relion-2

A Test Drive of the NVIDIA Jetson TX1 Developer Kit for Deep Learning and Computer Vision Applications

Compiler-Assisted Workload Consolidation For Efficient Dynamic Parallelism on GPU

DeepBE: Learning Deep Binary Encoding for Multi-Label Classification

Modified Levels of Parallel Odd-Even Transposition Sorting Network (OETSN) with GPU Computing using CUDA

Persistent RNNs: Stashing Recurrent Weights On-Chip

Parallel and Distributed Deep Learning

A Synchronization-Free Algorithm for Parallel Sparse Triangular Solves

Parallelizing Map Projection of Raster Data on Multi-core CPU and GPU Parallel Programming Frameworks

GPU Based Real-Time Welding Simulation with Smoothed-Particle Hydrodynamics

Efficient and High-quality Sparse Graph Coloring on the GPU

Tensor Contractions with Extended BLAS Kernels on CPU and GPU

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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