high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

Cody Rivera, Jieyang Chen, Nan Xiong, Shuaiwen Leon Song, Dingwen Tao

View

Tags: Algorithms, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, Tesla K40, Tesla M40, Tesla P100

February 16, 2020 by hgpu

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Alberto Zeni, Giulia Guidi, Marquita Ellis, Nan Ding, Marco D. Santambrogio, Steven Hofmeyr, Aydın Buluç, Leonid Oliker, Katherine Yelick

View

Download (PDF)

Source codes

Tags: Bioinformatics, Biology, CUDA, nVidia, Sequence alignment, Tesla V100

February 16, 2020 by hgpu

Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems

Mirko Myllykoski, Carl Christian Kjelgaard Mikkelsen

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Mathematical Software, nVidia, Package, Tesla V100

February 16, 2020 by hgpu

Working With Incremental Spatial Data During Parallel (GPU) Computation

Robert Chisholm

View

Download (PDF)

Source codes

Tags: Agent-based modeling, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan V, Package, Thesis

February 9, 2020 by hgpu

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

Ahmed E. Helal

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance, Tesla M2050, Thesis

February 9, 2020 by hgpu

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

Andi Drebes, Lorenzo Chelini, Oleksandr Zinenko, Albert Cohen, Henk Corporaal, Tobias Grosser, Kanishkan Vadivel, Nicolas Vasilache

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, Machine learning

February 9, 2020 by hgpu

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

Ji Liu, Abdullah-Al Kafi, Xipeng Shen, Huiyang Zhou

View

Download (PDF)

Tags: Compilers, Computer science, FPGA, nVidia, nVidia GeForce RTX 2080, OpenCL, Performance

February 9, 2020 by hgpu

A Language for Describing Optimization Strategies

Bastian Hagedorn, Johannes Lenfers, Thomas Koehler, Sergei Gorlatch, Michel Steuwer

View

Download (PDF)

Source codes

Tags: Computer science, DSL, High-level Languages, OpenCL, Package, Scala

February 9, 2020 by hgpu

GPU-accelerated dynamic programming for join-order optimization

Andreas Meister, Gunter Saake

View

Download (PDF)

Tags: Computer science, Databases, nVidia, OpenCL, Tesla K20

February 2, 2020 by hgpu

Optimization of a discontinuous Galerkin solver with OpenCL and StarPU

Bérenger Bramas, Philippe Helluy, Laura Mendoza, Bruno Weber

View

Download (PDF)

Source codes

Tags: Algorithms, Mathematics, nVidia, nVidia GeForce GTX 1050, OpenCL, Package, PDEs, Tesla P100

February 2, 2020 by hgpu

Non-Determinism in TensorFlow ResNets

Miguel Morin, Matthew Willetts

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, nVidia, Package, TensorFlow, Tesla M60

February 2, 2020 by hgpu

Interoperable GPU Kernels as Latency Improver for MEC

Juuso Haavisto, Jukka Riekki

View

Download (PDF)

Source codes

Tags: Computer science, GLSL, Graphics, nVidia, nVidia GeForce RTX 2080, nVidia Jetson TX2, Package, Vulkan

February 2, 2020 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Experiences with implementing Kokkos’ SYCL backend

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

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

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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems

Working With Incremental Spatial Data During Parallel (GPU) Computation

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

A Language for Describing Optimization Strategies

GPU-accelerated dynamic programming for join-order optimization

Optimization of a discontinuous Galerkin solver with OpenCL and StarPU

Non-Determinism in TensorFlow ResNets

Interoperable GPU Kernels as Latency Improver for MEC

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

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

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

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

Most viewed papers (last 30 days)