This paper presents GRAPE, a parallel system for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole. Underlying GRAPE are a simple programming model and a principled approach, based on partial evaluation and incremental computation. We show that sequential graph algorithms can be "plugged into" GRAPE with minor changes, and get parallelized. As long as the sequential algorithms are correct, their GRAPE parallelization guarantees to terminate with correct answers under a monotonic condition. Moreover, we show that algorithms in MapReduce, BSP and PRAM can be optimally simulated on GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-ofthe-art graph systems, using real-life and synthetic graphs.

This paper presents Zidian, a middleware for key-value (KV) stores to speed up SQL query evaluation over NoSQL. As opposed to common practice that takes a tuple id or primary key as key and the entire tuple as value, Zidian proposes a block-as-a-value model BaaV. BaaV represents a relation as keyed blocks ( k, B ), where k is a key of a block (a set) B of partial tuples. We extend relational algebra to BaaV. We show that under BaaV, Zidian substantially reduces data access and communication cost. We provide characterizations (sufficient and necessary conditions) for (a) result-preserving queries, i.e., queries covered by available BaaV stores, (b) scan-free queries, i.e., queries that can be evaluated without scanning any table, and (c) bounded queries, i.e., queries that can be answered by accessing a bounded amount of data. We show that in parallel processing, Zidian guarantees (a) no scans for scan-free queries, (b) bounded communication cost for bounded queries; and (c) parallel scalability, i.e., speed up when adding processors. Moreover, Zidian can be plugged into existing SQL-over-NoSQL systems and retains horizontal scalability. Using benchmark and real-life data, we empirically verify that Zidian improves existing SQL-over-NoSQL systems by 2 orders of magnitude on average.

This paper presents GRAPE, a parallel GRAPh Engine for graph computations. GRAPE differs from previous graph systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithms into a new model. Underlying GRAPE are a simple programming model, and a principled approach based on fixpoint computation with partial evaluation and incremental computation. Under a monotonic condition, GRAPE guarantees to converge at correct answers as long as the sequential algorithms are correct. We show how our familiar sequential graph algorithms can be parallelized by GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-theart graph systems, using real-life and synthetic graphs.

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