The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

There has been a growing interest in the graph-streaming setting where a continuous
stream of graph updates is mixed with graph queries. In principle, purely-functional trees are …

Various applications model problems as streaming graphs, which need to quickly apply a
stream of updates and run algorithms on the updated graph. Furthermore, many dynamic …

Writing high-performance GPU implementations of graph algorithms can be challenging. In
this paper, we argue that three optimizations called throughput optimizations are key to high …

By abstracting away the complexity of distributed systems, large-scale data processing
platforms—MapReduce, Hadoop, Spark, Dryad, etc.—have provided developers with simple …

Parallel graph analytics Page 1 78 COMMUNICATIONS OF THE ACM | MAY 2016 | VOL. 59 |
NO. 5 contributed articles DOI:10.1145/2901919 Data-centric abstractions and execution …

Compilation techniques for nested-parallel applications that can adapt to hardware and
dataset characteristics are vital for unlocking the power of modern hardware. This paper …

Multicore systems must exploit locality to scale, scheduling tasks to minimize data
movement. While locality-aware parallelism is well studied in non-speculative systems, it …

The trend towards processor heterogeneity and distributed-memory has significantly
increased the complexity of parallel programming. In addition, the mix of applications that …

We present the SSTGraph framework for the storage and analysis of dynamic graphs. Its
performance matches or exceeds state-of-the-art static graph engines and supports …