Making caches work for graph analytics

Abstract

Abstract-Large-scale applications implemented in today's high performance graph frameworks heavily underutilize modern hardware systems. While many graph frameworks have made substantial progress in optimizing these applications, we show that it is still possible to achieve up to 5× speedups over the fastest frameworks by greatly improving cache utilization. Previous systems have applied out-of-core processing techniques from the memory/disk boundary to the cache/DRAM boundary. However, we find that blindly applying such techniques is ineffective because the much smaller performance gap between cache and DRAM requires new designs for achieving scalable performance and low overhead. We present Cagra, a cache optimized inmemory graph framework. Cagra uses a novel technique, CSR Segmenting, to break the vertices into segments that fit in last level cache, and partitions the graph into subgraphs based on the segments. Random accesses in each subgraph are limited to one segment at a time, eliminating the much slower random accesses to DRAM. The intermediate updates from each subgraph are written into buffers sequentially and later merged using a low overhead parallel cache-aware merge. Cagra achieves speedups of up to 5× for PageRank, Collaborative Filtering, Label Propagation and Betweenness Centrality over the best published results from state-of-the-art graph frameworks, including GraphMat, Ligra and GridGraph.

Domain-Specialized Cache Management for Graph Analytics

“…A distinguishing property of natural graphs is the skew in their degree distribution [2,3,19,45,65,66]. The skew Table I ROWS #2 AND #4 SHOW THE PERCENTAGE OF VERTICES HAVING DEGREE EQUAL OR GREATER THAN THE AVERAGE (I.E., HOT VERTICES), WITH RESPECT TO IN-EDGES AND OUT-EDGES, RESPECTIVELY; THE HIGHER THE SKEW, THE LOWER THE PERCENTAGE.…”

“…Second, they should minimize disruption to the underlying graph structure, specifically for graphs that exhibit community structure. Prior works have noted that vertex order for many real-world graph datasets closely follow underlying community structure, meaning vertices from the same community are ordered close by in memory, exhibiting good spatio-temporal locality that should be preserved [2,3,19].…”

“…Graph analytics commonly process large graph datasets whose main memory footprint span tens to hundreds of gigabytes [4,54]. When processing such large graphs, graph-analytic applications exhibit a lack of cache locality, leading to frequent misses in on-chip caches, compromising application performance [2,3,19,20,30,31,36].…”

“…A distinguishing property of graph datasets common in many graph-analytic applications is that the vertex degrees follow a skewed power-law distribution, in which a small fraction of vertices have many connections while the majority of vertices have relatively few connections [2,3,19,45,65,66]. Graphs characterized by such a distribution are known as natural or scale-free graphs and are prevalent in a variety of domains, including social networks, computer networks, financial networks, semantic networks, and airline networks.…”

“…When hot vertices share the same cache block with cold vertices, valuable cache space is underutilized. Existing software techniques [2,3,19] solve the latter problem by reordering vertices in the memory space, such that hot vertices share cache blocks with other hot vertices. However, the former problem of protecting hot vertices from premature evictions remains an open challenge, even for the state-of-the-art thrash-resistant hardware cache management schemes.…”