SIGPLAN

Many modern workloads compute on large amounts of data, often with irregular memory accesses. Current architectures perform poorly for these workloads, as existing prefetching techniques cannot capture the memory access patterns; these applications end ...

The importance of irregular applications such as graph analytics is rapidly growing with the rise of Big Data. However, parallel graph workloads tend to perform poorly on general-purpose chip multiprocessors (CMPs) due to poor cache locality, low ...

Many important graph applications are iterative algorithms that repeatedly process the input graph until convergence. For such algorithms, graph abstraction is an important technique: although much smaller than the original graph, it can bootstrap an ...

Graph analytics delivers deep knowledge by processing large volumes of highly connected data. In real-world graphs, the degree distribution tends to follow the power law -- a small portion of nodes own a large number of neighbors. The high irregularity ...