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Accelerating Workloads on FPGAs via OpenCL: A Case Study with OpenDwarfs

Anshuman Verma, Ahmed E. Helal, Konstantinos Krommydas, Wu-Chun Feng

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Tags: Computer science, FPGA, Intel Xeon Phi, N-body simulation, nVidia, OpenCL, performance portability, Tesla C2070, Tesla K20

January 26, 2017 by hgpu

Deep Convolutional Network evaluation on the Intel Xeon Phi: Where Subword Parallelism meets Many-Core

Gaurav Raina

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Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Thesis

January 26, 2017 by hgpu

Multi-core parallelism in a column-store

Mrunal M. Gawade

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Tags: Computer science, Databases, Intel Xeon Phi, Thesis

January 23, 2017 by hgpu

Xeon Phi: A comparison between the newly introduced MIC architecture and a standard CPU through three types of problems

Joakim Kristiansen

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

January 19, 2017 by hgpu

An N log N Parallel Fast Direct Solver for Kernel Matrices

Chenhan D. Yu, William B. March, George Biros

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Tags: Algorithms, Computational Complexity, Computer science, Factorization, Intel Xeon Phi, Machine learning, Package, Performance

January 16, 2017 by hgpu

Software Prefetching for Indirect Memory Accesses

Sam Ainsworth, Timothy M. Jones

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Tags: ARM, Benchmarking, Compilers, Computer science, Intel Xeon Phi, LLVM, MPI, Package, Prefetch

January 10, 2017 by hgpu

A Framework for Dense Triangular Matrix Kernels on Various Manycore Architectures

Ali Charara, David Keyes, Hatem Ltaief

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Tags: BLAS, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, Matrix multiplication, nVidia, Package, Tesla K40

January 8, 2017 by hgpu

High productivity multi-device exploitation with the Heterogeneous Programming Library

Moises Vinas, Basilio B. Fraguela, Diego Andrade, Ramon Doallo

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, load balancing, nVidia, OpenCL, Package, Portability, Tesla K20, Tesla M2050

December 22, 2016 by hgpu

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Fabio Baruffa, Luigi Iapichino, Nicolay J. Hammer, Vasileios Karakasis

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Tags: Algorithms, Astrophysics, Computational Physics, Computer science, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, OpenMP, Performance, Physics

December 20, 2016 by hgpu

Automating the Last-Mile for High Performance Dense Linear Algebra

Richard Michael Veras, Tze Meng Low, Tyler Michael Smith, Robert van de Geijn, Franz Franchetti

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Tags: Code generation, Computer science, Intel Xeon Phi, Linear Algebra, Performance

December 14, 2016 by hgpu

Performance Evaluation and Optimization of HPCG benchmark on CPU + MIC platform

Qingyi Pan, Xiaoying Wang

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, OpenMP, Performance

December 10, 2016 by hgpu

BrainFrame: A heterogeneous accelerator platform for neuron simulations

Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harrys Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis

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Tags: Biophysics, Computer science, Heterogeneous systems, Intel Xeon Phi, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX Titan X, OpenMP

December 10, 2016 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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