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ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

Anderson Faustino da Silva, Bruno Conde Kind, Jose Wesley de Souza Magalhaes, Jeronimo Nunes Rocha, Breno Campos Ferreira Guimaraes, Fernando Magno Quintao Pereira

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Tags: Algorithms, Benchmarking, Code generation, Compiler, Computer science, LLVM, Package

December 20, 2020 by hgpu

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Xuewen Cui

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Tags: Artificial intelligence, Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, OpenMP, Performance, Tesla K40, Tesla P100, Tesla V100, Thesis

December 20, 2020 by hgpu

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

Markus Holzer, Martin Bauer, Harald Kostler, Ulrich Rüde

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Tags: Algorithms, cfd, Code generation, CUDA, Fluid dynamics, Lattice Boltzmann model, MPI, nVidia, OpenCL, Package, Physics, Programming techniques, Tesla P100, Tesla V100

December 20, 2020 by hgpu

Solving large permutation flow-shop scheduling problems on GPU-accelerated supercomputers

Jan Gmys

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Tesla V100

December 20, 2020 by hgpu

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang, Sanjay Choudhry

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Tags: cfd, CUDA, Fluid dynamics, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia A100, Partial differential equations, PDEs, Physics, Tesla V100, Video

December 20, 2020 by hgpu

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

Yao Wan, Yang He, Jian-Guo Zhang, Yulei Sui, Hai Jin, Guandong Xu, Caiming Xiong, Philip S. Yu

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Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Package, Programming Languages, Python, Video

December 13, 2020 by hgpu

Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing

Akshay Dua, Yixing Li, Fengbo Ren

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Tags: Cloud, Computer science, DSP, FPGA, Hardware Architecture, Neural networks, OpenCL, Package

December 13, 2020 by hgpu

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Task scheduling, Tesla V100

December 13, 2020 by hgpu

EasyPBR: A Lightweight Physically-Based Renderer

Radu Alexandru Rosu, Sven Behnke

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Tags: 3D Graphics and Realism, Deep learning, nVidia, nVidia GeForce RTX 2060, OpenGL, Package, Python, Rendering, Visualization

December 13, 2020 by hgpu

Efficient code generation for hardware accelerators by refining partially specified implementation

Ulysse Beaugnon

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Tags: Algorithms, Code generation, Computer science, CUDA, Linear Algebra, nVidia, Optimization, Package, Thesis

December 13, 2020 by hgpu

Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

Chao Jiang, David Ojika, Sofia Vallecorsa, Thorsten Kurth, Prabhat, Bhavesh Patel, Herman Lam

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks

December 6, 2020 by hgpu

Exploring FPGA Optimizations to Compute Sparse Numerical Linear Algebra Kernels

Federico Favaro, Ernesto Dufrechou, Pablo Ezzatti, Juan P. Oliver

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Tags: Computer science, FPGA, Linear Algebra, OpenCL, Optimization, Performance, Sparse

December 6, 2020 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

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