Sampa (uwsampa)

Sampa

uwsampa

Geek Repo

Computer Architecture Lab @ University of Washington

Location:Seattle, WA

Home Page:https://sampa.cs.washington.edu

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Sampa's repositories

research-group-web

a template for research group sites

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grappa

Grappa: scaling irregular applications on commodity clusters

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cse548-labs

A repository containing homework labs for CSE548

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accept

an approximate compiler

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hfbs

a hardware-friendly bilateral solver

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graphbench

A graph application benchmark suite

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giraph-docker

Giraph Docker image

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coredet

A Compiler and Runtime System for Deterministic Multithreaded Execution

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sampa-public

the Sampa group website

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accept-apps

benchmarks for ACCEPT

grappa-docker

Dockerfiles for Grappa

nltk_hadoop

NLTK on hadoop

zynqWrapper

A low-latency and simple hardware interface for the Zynq SoC complemented with software driver code

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Kernels

This is a set of simple programs that can be used to explore the features of a parallel platform.

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dmp-os

Deterministic process groups

mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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nntune

neural network configuration space explorer

cse590g

Source for CSE 590G (Architecture Seminar)

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grappa-doxygen

Doxygen-generated documentation for Grappa

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grappa-www

Grappa project website

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inputcovsched

This code formed the basis of "Input-Covering Schedules for Multithreaded Programs (OOPSLA 2013)" and "Symbolic Execution of Multithreaded Programs from Arbitrary Program Contexts (OOPSLA 2014)".

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MonetDB

This is the official mirror of the MonetDB Mercurial repository. Please note that we do not accept pull requests on github. The regresession test results can be found on the MonetDB Testweb http://monetdb.cwi.nl/testweb/web/status.php .For contributions please see: https://www.monetdb.org/Developers

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parallel-programming-course

A version of CSEP524 based around the Intel Xeon Phi platform

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sampa

Common sampa group stuff

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training_results_v0.7

MLPerf™ Training v0.7 results

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