high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia

Synthesizing Benchmarks for Predictive Modeling

Chris Cummins, Pavlos Petoumenos, Zheng Wang, Hugh Leather

View

Download (PDF)

Source codes

Tags: ATI, ATI Radeon HD 7970, Benchmarking, Code generation, Compilers, Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, OpenCL, Package

December 31, 2016 by hgpu

Automatic OpenCL Task Adaptation for Heterogeneous Architectures

Pierre Huchant, Marie-Christine Counilh, Denis Barthou

View

Download (PDF)

Tags: Compilers, Computer science, Heterogeneous systems, nVidia, nVidia Quadro K5000, OpenCL, Tesla K20, Tesla M2075

December 31, 2016 by hgpu

Android Malware Classification Using Parallelized Machine Learning Methods

Lifan Xu

View

Download (PDF)

Tags: ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, Neural networks, nVidia, OpenCL, OpenMP, Security, Tesla C2050, Thesis

December 31, 2016 by hgpu

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Kaipeng Li, Amanullah Ghazi, Chance Tarver, Jani Boutellier, Mahmoud Abdelaziz, Lauri Anttila, Markku Juntti, Mikko Valkama, Joseph R. Cavallaro

View

Download (PDF)

Tags: ARM, CUDA, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, Signal processing, SoC

December 31, 2016 by hgpu

Language Modeling with Gated Convolutional Networks

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

View

Download (PDF)

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

December 26, 2016 by hgpu

Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization

M. A. Clark, Balint Joo, Alexei Strelchenko, Michael Cheng, Arjun Gambhir, Richard Brower

View

Download (PDF)

Tags: Algorithms, Computational Physics, CUDA, High Energy Physics - Lattice, nVidia, Physics, QCD, Tesla K20

December 26, 2016 by hgpu

Streaming GPU Singular Value and Dynamic Mode Decompositions

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

View

Download (PDF)

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

View

Download (PDF)

Source codes

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

View

Download (PDF)

Source codes

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

December 22, 2016 by xhgpu

An EoS-meter of QCD transition from deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stocker, Xin-Nian Wang

View

Download (PDF)

Tags: CUDA, Deep learning, High Energy Physics - Phenomenology, Keras, Machine learning, Neural networks, Nuclear Theory, nVidia, Physics, QCD, TensorFlow, Tesla K20

December 20, 2016 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Synthesizing Benchmarks for Predictive Modeling

Automatic OpenCL Task Adaptation for Heterogeneous Architectures

Android Malware Classification Using Parallelized Machine Learning Methods

Parallel Digital Predistortion Design on Mobile GPU and Embedded Multicore CPU for Mobile Transmitters

Language Modeling with Gated Convolutional Networks

Batched Shift Reduce Parsing with Lists of Vectors on CUDA

Function Call Re-Vectorization

Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization

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

An EoS-meter of QCD transition from deep learning

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

Most viewed papers (last 30 days)