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CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li, Wei Tan

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Tags: Algorithms, Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Package, Tesla V100

March 17, 2018 by hgpu

Improved OpenCL-based Implementation of Social Field Pedestrian Model

Bin Yu, Ke Zhu, Kaiteng Wu, Michael Zhang

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Tags: Algorithms, Computer science, OpenCL

March 17, 2018 by hgpu

NVIDIA Tensor Core Programmability, Performance & Precision

Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Mixed precision, nVidia, Performance, Tesla V100

March 17, 2018 by hgpu

OpenCL-ready High Speed FPGA Network for Reconfigurable High Performance Computing

Ryohei Kobayashi, Yuma Oobata, Norihisa Fujita, Yoshiki Yamaguchi, Taisuke Boku

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Tags: Algorithms, Computer science, FPGA, Heterogeneous systems, OpenCL

March 10, 2018 by hgpu

Multithreaded Dense Linear Algebra on Asymmetric Multi-core Processors

Sandra Catalan Pallares

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Tags: ARM, Benchmarking, Computer science, Factorization, Linear Algebra, Thesis

March 10, 2018 by hgpu

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Lu Li

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla C1060, Tesla C2050, Tesla K20, Tesla M2050, Thesis

March 10, 2018 by hgpu

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Moritz Kreutzer, Georg Hager, Dominik Ernst, Holger Fehske, Alan R. Bishop, Gerhard Wellein

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Tags: Chemistry, Computational Physics, Computer science, CUDA, Intel Xeon Phi, nVidia, OpenMP, Package, Performance, Physics, Quantum Physics, Tesla P100

March 10, 2018 by hgpu

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

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Tags: Compilers, Computer science, DSL, FPGA, Matrix multiplication, nVidia, OpenMPI, Performance, Programming Languages, PTX, Tesla K40

March 3, 2018 by hgpu

OpenCL Acceleration for TensorFlow

Mehdi Goli, Luke Iwanski, John Lawson, Uwe Dolinsky, Andrew Richards

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Tags: ARM, Computer science, Deep learning, Machine learning, OpenCL, Performance, SYCL, TensorFlow

March 3, 2018 by hgpu

Equalizer 2.0 – Convergence of a Parallel Rendering Framework

Stefan Eilemann, David Steiner, Renato Pajarola

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Tags: Algorithms, Computer science, Graphics, nVidia, nVidia GeForce GTX 970, OpenGL, Package, Rendering, Virtual reality, Visualization

March 3, 2018 by hgpu

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

Alfonso Rodriguez, Cesar Castanares, Teresa Riesgo, Eduardo de la Torre

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Tags: ARM, Computer science, FPGA, nVidia, nVidia GeForce GTX Titan X, OpenCL

February 17, 2018 by hgpu

Long-time Simulations with Complex Code Using Multiple Nodes of Intel Xeon Phi Knights Landing

Jonathan S. Graf, Matthias K. Gobbert, Samuel Khuvis

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Tags: Computer science, Differential equations, Intel Xeon Phi, MPI, OpenMP, Partial differential equations, PDEs

February 17, 2018 by hgpu

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

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Improved OpenCL-based Implementation of Social Field Pedestrian Model

NVIDIA Tensor Core Programmability, Performance & Precision

OpenCL-ready High Speed FPGA Network for Reconfigurable High Performance Computing

Multithreaded Dense Linear Algebra on Asymmetric Multi-core Processors

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

OpenCL Acceleration for TensorFlow

Equalizer 2.0 – Convergence of a Parallel Rendering Framework

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

Long-time Simulations with Complex Code Using Multiple Nodes of Intel Xeon Phi Knights Landing

Recent source codes

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

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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