With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques that can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM and uses a paging-like technique to load sub graphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16, 000+ cores.

Subscripts using induction variables that cannot be expressed as a formula in terms of the enclosing-loop indices appear in the low-level implementation of common programming abstractions such as Alter, or stack operations and pose significant challenges to automatic parallelization. Because the complexity of such induction variables is often due to their conditional evaluation across the iteration space of loops we name them Conditional Induction Variables (CIV). This paper presents a flow-sensitive technique that summarizes both such CIV-based and affine subscripts to program level, using the same representation. Our technique requires no modifications of our dependence tests, which is agnostic to the original shape of the subscripts, and is more powerful than previously reported dependence tests that rely on the pairwise disambiguation of read-write references. We have implemented the CIV analysis in our parallelizing compiler and evaluated its impact on five Fortran benchmarks. We have found that that there are many important loops using CIV subscripts and that our analysis can lead to their scalable parallelization. This in turn has led to the parallelization of the benchmark programs they appear in.

Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5 × - 8× improvement for a variety of graph algorithms at 12, 000+ cores.