high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Language Modeling with Gated Convolutional Networks

Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Performance, Tesla M40, Torch

December 26, 2016 by hgpu

Batched Shift Reduce Parsing with Lists of Vectors on CUDA

Andrew Drozdov

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, nVidia GeForce GTX Titan Black, Package, Torch

December 26, 2016 by hgpu

Function Call Re-Vectorization

Rubens E. A. Moreira, Sylvain Collange, Fernando Magno Quintao Pereira

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Package, String matching

December 26, 2016 by hgpu

Streaming GPU Singular Value and Dynamic Mode Decompositions

Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton

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Tags: Algorithms, Computer vision, CUDA, Mathematics, Matrix multiplication, Numerical Analysis, nVidia, Sparse matrix, Tesla K40

December 26, 2016 by hgpu

High productivity multi-device exploitation with the Heterogeneous Programming Library

Moises Vinas, Basilio B. Fraguela, Diego Andrade, Ramon Doallo

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, load balancing, nVidia, OpenCL, Package, Portability, Tesla K20, Tesla M2050

December 22, 2016 by hgpu

Log File Regular Expression Pattern Matching And Capture With GPUs

António Luís Pinto Silva

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M, Package, pattern matching, Thesis

December 22, 2016 by xhgpu

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Fabio Baruffa, Luigi Iapichino, Nicolay J. Hammer, Vasileios Karakasis

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Tags: Algorithms, Astrophysics, Computational Physics, Computer science, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, OpenMP, Performance, Physics

December 20, 2016 by hgpu

Accelerating Spark RDD Operations with Local and Remote GPU Devices

Yasuhiro Ohno, Shin Morishima, Hiroki Matsutani

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 980 Ti, Spark

December 20, 2016 by hgpu

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Farhad Merchant, Tarun Vatwani, Anupam Chattopadhyay, Soumyendu Raha, S K Nandy, Ranjani Narayan

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Tags: Algorithms, Computer science, CUDA, Factorization, FPGA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Performance, Tesla C2050

December 17, 2016 by hgpu

Toward Automatic Translation: From OpenACC to OpenMP 4

Nawrin Sultana

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Tags: Algorithms, Code generation, Compilers, Computer science, nVidia, OpenACC, OpenMP, Tesla K40, Thesis

December 17, 2016 by hgpu

Automating the Last-Mile for High Performance Dense Linear Algebra

Richard Michael Veras, Tze Meng Low, Tyler Michael Smith, Robert van de Geijn, Franz Franchetti

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Tags: Code generation, Computer science, Intel Xeon Phi, Linear Algebra, Performance

December 14, 2016 by hgpu

Translating OpenMP Device Constructs to OpenCL using Unnecessary Data Transfer Elimination

Junghyun Kim, Yong-Jun Lee, Jungho Park, Jaejin Lee

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Tags: AMD Radeon R9 390X, ATI, Benchmarking, Code generation, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenACC, OpenCL, OpenMP

December 14, 2016 by hgpu

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Language Modeling with Gated Convolutional Networks

Batched Shift Reduce Parsing with Lists of Vectors on CUDA

Function Call Re-Vectorization

Streaming GPU Singular Value and Dynamic Mode Decompositions

High productivity multi-device exploitation with the Heterogeneous Programming Library

Log File Regular Expression Pattern Matching And Capture With GPUs

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Accelerating Spark RDD Operations with Local and Remote GPU Devices

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Toward Automatic Translation: From OpenACC to OpenMP 4

Automating the Last-Mile for High Performance Dense Linear Algebra

Translating OpenMP Device Constructs to OpenCL using Unnecessary Data Transfer Elimination

Recent source codes

Awesome LLM-Driven Kernel Generation

PhysProver: Advancing Automatic Theorem Proving for Physics

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

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