ACM Transactions on

Parallel Computing (TOPC)

Latest Articles

A High-Quality and Fast Maximal Independent Set Implementation for GPUs

Computing a maximal independent set is an important step in many parallel graph algorithms. This article introduces ECL-MIS, a maximal independent set... (more)

Scheduling Dynamic Parallel Workload of Mobile Devices with Access Guarantees

We study a dynamic resource-allocation problem that arises in various parallel computing scenarios, such as mobile cloud computing, cloud computing... (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

New High Performance GPGPU Code Transformation Framework Applied to Large Production Weather Prediction Code

An Autotuning Protocol to Rapidly Build Autotuners

BARAN: Bimodal Adaptive Reconfigurable-Allocator Network-on-Chip

Lock Contention Management in Multithreaded MPI

PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs

Natural graphs with skewed distribution raise unique challenges to distributed graph computation and partitioning. Existing graph-parallel systems usually use a "one size fits all" design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab), or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX).
In this paper, we argue that skewed distribution in natural graphs also calls for differentiated processing on high-degree and low-degree vertices. We then introduce PowerLyra, a new distributed graph processing system that embraces the best of both worlds of existing graph-parallel systems, by dynamically applying different computation and partitioning strategies for different vertices. PowerLyra further provides an efficient hybrid graph partitioning algorithm (hybrid-cut) that combines edge-cut and vertex-cut with heuristics. Based on PowerLyra, we design locality-conscious data layout optimization to improve cache locality of graph accesses during communication. PowerLyra is implemented based on the latest GraphLab and can seamlessly support various graph algorithms running in both synchronous and asynchronous execution modes. A detailed evaluation on three clusters using various graph-analytics and MLDM (machine learning and data mining) applications show that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real-world and synthetic graphs accordingly, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid-cut to GraphX further confirms the efficiency and generality of PowerLyra.

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