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On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Moritz Lehmann, Mathias J. Krause, Giorgio Amati, Marcello Sega, Jens Harting, Stephan Gekle

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Tags: AMD Radeon Instinct MI100, AMD Radeon VI, ATI, Fluid dynamics, lattice Boltzmann, Mixed precision, nVidia, OpenCL, Tesla K20, Tesla K40, Tesla K80, Tesla P100, Tesla V100

December 19, 2021 by hgpu

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Evaluation of Rust for GPGPU high performance computing

Evaluation of Rust for GPGPU high-performance computing

SCAMP: CPU/GPU Implementation of the SCAMP algorithm for computing the matrix profile

Optimization of massive data applications on heterogeneous architectures

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

FDAS: Fourier Domain Acceleration Search, FPGA-accelerated with OpenCL

Pulsar search acceleration using FPGAs and OpenCL templates

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Reeber: a library for shared- and distributed-memory parallel computation of merge trees

Fast Merge Tree Computation via SYCL

pocl: Portable Computing Language

Efficient OpenCL system integration of non-blocking FPGA accelerators

OneFlow: a deep learning framework designed to be user-friendly, scalable and efficient

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

Python Suite for Advanced General Ensemble Simulations

PySAGES: flexible, advanced sampling methods accelerated with GPUs

GPU load-balancing library for regular and irregular computations

A Programming Model for GPU Load Balancing

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