ACM Transactions on

Parallel Computing (TOPC)

Latest Articles

Group Mutual Exclusion by Fetch-and-increment

The group mutual exclusion (GME) problem (also called the room synchronization problem) arises in various practical applications that require concurrent data sharing. Group mutual exclusion aims to achieve exclusive access to a shared resource (a shared room) while facilitating concurrency among non-conflicting requests. The problem is that threads... (more)

Concurrent Hash Tables: Fast and General(?)!

Concurrent hash tables are one of the most important concurrent data structures, which are used in numerous applications. For some applications, it is common that hash table accesses dominate the execution time. To efficiently solve these problems in parallel, we need implementations that achieve speedups in highly concurrent scenarios.... (more)


ACM Transactions on Parallel Computing Names David Bader as Editor-in-Chief

ACM Transactions on Parallel Computing (TOPC) welcomes David Bader as new Editor-in-Chief, for the term November 1, 2018 to October 31, 2021. David is a Professor and Chair in the School of Computational Science and Engineering and College of Computing at Georgia Institute of Technology.


About TOPC

ACM Transactions on Parallel Computing (TOPC) is a forum for novel and innovative work on all aspects of parallel computing, including foundational and theoretical aspects, systems, languages, architectures, tools, and applications. It will address all classes of parallel-processing platforms including concurrent, multithreaded, multicore, accelerated, multiprocessor, clusters, and supercomputers. READ MORE

Forthcoming Articles

Optimizing I/O Performance of HPC Applications with Autotuning

EagerMap: A task mapping algorithm to improve communication and load balancing in clusters of multicore systems

Communication between tasks has been identified as a major challenge for the performance and energy efficiency of parallel applications.
A common way to improve communication is to increase its locality, that is, to reduce the distances of data transfers, prioritizing the usage of faster and more efficient local interconnections over remote ones.
An important problem to be solved in this context is how to determine an optimized mapping of tasks to cluster nodes and cores that increases the overall locality.
In this paper, we propose the EagerMap algorithm to determine task mappings, which is based on a greedy heuristic to match application communication patterns to hardware hierarchies.
Compared to previous algorithms, EagerMap is faster, scales better, and supports more types of computer systems, while maintaining the same quality of the determined task mapping.
EagerMap is therefore an interesting choice for task mapping on a variety of modern parallel architectures.

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