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DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

Weihua Guo, You Xu, Xueyang Feng

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Tags: Biology, Computer science, Deep learning, Medicine, nVidia, nVidia GeForce GTX 1080, Package, Python, TensorFlow

May 11, 2017 by hgpu

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Xinyu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado

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Tags: Algorithms, Computer science, Computer vision, Deep learning, DSP, FPGA, Neural networks

May 11, 2017 by hgpu

Resource-Aware Just-in-Time OpenCL Compiler for Coarse-Grained FPGA Overlays

Abhishek Kumar Jain, Douglas L. Maskell, Suhaib A. Fahmy

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Tags: ARM, Computer science, DSP, FPGA, Heterogeneous systems, OpenCL

May 11, 2017 by hgpu

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Konstantinos Krommydas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Performance, Tesla K20, Thesis

May 9, 2017 by hgpu

Efficient Parallel Strategy Improvement for Parity Games

John Fearnley

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Logic in Computer Science, nVidia, nVidia GeForce GTX 780, Package

May 9, 2017 by hgpu

Fast Sorting Algorithms using AVX-512 on Intel Knights Landing

Berenger Bramas

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Tags: Algorithms, Computer science, Intel Xeon Phi, OpenMP, Package, Sorting

May 9, 2017 by hgpu

Acceleration of Deep Learning on FPGA

Huyuan Li

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Tags: CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, OpenCL, Tesla K40, Thesis

May 9, 2017 by hgpu

Multidimensional upwind hydrodynamics on unstructured meshes using Graphics Processing Units I. Two-dimensional uniform meshes

Sijme-Jan Paardekooper

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Tags: Algorithms, Astrophysics, cfd, Computational Physics, CUDA, Fluid dynamics, Instrumentation and Methods for Astrophysics, nVidia, Package, Tesla K20, Triangular meshes

May 9, 2017 by hgpu

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Stephen W. Keckler

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Tags: Compression, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Virtualization

May 6, 2017 by hgpu

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Antti-Pekka Hynninen, Dmitry I. Lyakh

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Tesla K20, Tesla K40, Tesla M40, Tesla P100

May 6, 2017 by hgpu

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

Tatyana Dyakonova, Alexander Khoperskov, Sergey Khrapov

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Tags: cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, Tesla C2070, Tesla K20, Tesla K40, Tesla K80

May 6, 2017 by hgpu

GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

Zheyong Fan, Ville Vierimaa, Ari Harju

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Tags: Computational Physics, CUDA, nVidia, Package, Physics, Sparse matrix, Tesla K40

May 6, 2017 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

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high performance computing on graphics processing units: hgpu.org

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DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Resource-Aware Just-in-Time OpenCL Compiler for Coarse-Grained FPGA Overlays

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Efficient Parallel Strategy Improvement for Parity Games

Fast Sorting Algorithms using AVX-512 on Intel Knights Landing

Acceleration of Deep Learning on FPGA

Multidimensional upwind hydrodynamics on unstructured meshes using Graphics Processing Units I. Two-dimensional uniform meshes

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

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

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