This week Convey Computer announced six entries on the Graph500 List that double the performance of prior posted results. On the most recent list, multiple Convey single-node, hybrid-core systems clocked in at between 1.60 to 1.76 GTEP/s (billion edges per second) on problem sizes 27 and 28. The Graph500 organization establishes and maintains a set of large-scale benchmarks that measure performance of “big data” applications.
Convey cited two reasons for the significant performance improvement: a new “Breadth-First Search” (BFS)personality and a graph-optimized memory crossbar design. Convey developed a “personality” specific to the BFS algorithm which leverages Convey’s balanced architecture. The personality contains multiple function pipes (implemented in hardware on the system’s FPGAs), and typically has thousands of loads in flight simultaneously. It also manages the synchronization of stores to memory as required by the Graph500 benchmark.
There is little doubt that memory systems are an ‘Achilles Heel’ of handling big data applications. Today’s commodity systems are optimized for sequential memory accesses, not the random accesses typically found in graph problems. This really hurts performance when processing large-scale analytics applications,” explained Bruce Toal, CEO and co-founder of Convey Computer. “Our hybrid-core solution combines a powerful memory subsystem, which is ideal for massive data analytics, and a graph-friendly architecture capable of managing multi-terabyte graphs with billions of nodes.”
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