Parallel Computing (TOPC)


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ACM Transactions on Parallel Computing (TOPC), Volume 3 Issue 2, August 2016

Sixteen Heuristics for Joint Optimization of Performance, Energy, and Temperature in Allocating Tasks to Multi-Cores
Hafiz Fahad Sheikh, Ishfaq Ahmad
Article No.: 9
DOI: 10.1145/2948973

Three-way joint optimization of performance (P), energy (E), and temperature (T) in scheduling parallel tasks to multiple cores poses a challenge that is staggering in its computational complexity. The goal of the PET...

Selecting Multiple Order Statistics with a Graphics Processing Unit
Jeffrey D. Blanchard, Erik Opavsky, Emircan Uysaler
Article No.: 10
DOI: 10.1145/2948974

Extracting a set of multiple order statistics from a huge data set provides important information about the distribution of the values in the full set of data. This article introduces an algorithm, bucketMultiSelect, for simultaneously selecting...

Identifying the Root Causes of Wait States in Large-Scale Parallel Applications
David Böhme, Markus Geimer, Lukas Arnold, Felix Voigtlaender, Felix Wolf
Article No.: 11
DOI: 10.1145/2934661

Driven by growing application requirements and accelerated by current trends in microprocessor design, the number of processor cores on modern supercomputers is increasing from generation to generation. However, load or communication imbalance...

Compiling Affine Loop Nests for a Dynamic Scheduling Runtime on Shared and Distributed Memory
Roshan Dathathri, Ravi Teja Mullapudi, Uday Bondhugula
Article No.: 12
DOI: 10.1145/2948975

Current de-facto parallel programming models like OpenMP and MPI make it difficult to extract task-level dataflow parallelism as opposed to bulk-synchronous parallelism. Task parallel approaches that use point-to-point...

Assessing General-Purpose Algorithms to Cope with Fail-Stop and Silent Errors
Anne Benoit, Aurélien Cavelan, Yves Robert, Hongyang Sun
Article No.: 13
DOI: 10.1145/2897189

In this article, we combine the traditional checkpointing and rollback recovery strategies with verification mechanisms to cope with both fail-stop and silent errors. The objective is to minimize makespan and/or energy consumption. For divisible...

Simple Parallel and Distributed Algorithms for Spectral Graph Sparsification
Ioannis Koutis, Shen Chen Xu
Article No.: 14
DOI: 10.1145/2948062

We describe simple algorithms for spectral graph sparsification, based on iterative computations of weighted spanners and sampling. Leveraging the algorithms of Baswana and Sen for computing spanners, we obtain the first distributed spectral...