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Graph algorithm is pervasive in many applications ranging from targeted advertising to natural language processing. Recently, Asynchronous Graph Processing (AGP) is becoming a promising model to support graph algorithm on large-scale distributed computing platforms because it enables faster convergence speed and lower synchronization cost than the synchronous model for no barrier between iterations. However, existing AGP methods still suffer from poor performance for inefficient vertex state propagation. In this paper, we propose an effective and low-cost forward and backward sweeping execution method to accelerate state propagation for AGP, based on a key observation that states in AGP can be propagated between vertices much faster when the vertices are processed sequentially along the graph path within each round. Through dividing graph into paths and asynchronously processing vertices on each path in an alternative forward and backward way according to their order on this path, vertex states in our approach can be quickly propagated to other vertices and converge in a faster way with only little additional overhead. In order to efficiently support it over distributed platforms, we also propose a scheme to reduce the communication overhead along with a static priority ordering scheme to further improve the convergence speed. Experimental results on a cluster with 1,024 cores show that our approach achieves excellent scalability for large-scale graph algorithms and the overall execution time is reduced by at least 39.8 percent, in comparison with the most cutting-edge methods. [ABSTRACT FROM PUBLISHER]

For large-scale graph analysis on a single PC, asynchronous processing methods are known to converge more quickly than the synchronous approach, because of more efficient propagation of vertices state. However, current asynchronous methods are still very suboptimal in propagating state across different graph partitions. This presents a bottleneck for cross-partition state update and slows down the convergence of the processing task. To tackle this problem, we propose a new method, named the HotGraph, to faster graph processing by extracting a backbone structure, called hot graph, that spans all the partitions of the original graph. With this approach, most cross-partition state propagations in traditional solutions now take place within only a few hot graph partitions, thus removing the cross-partition bottleneck. We also develop a partition scheduling algorithm to maximize the hot graph’s effectiveness by keeping it in memory and assigning it the highest priority for processing as much as possible. A forward and backward sweeping execution strategy is then proposed to further accelerate the convergence. Experimental results show that HotGraph can reduce the number of vertex state updates processed by 51.5 percent, compared with state-of-the-art schemes. Applying our optimizations further reduces this number by 72.6 percent and the execution time by 80.8 percent. [ABSTRACT FROM AUTHOR]

The huge amount of data enforces great pressure on the processing efficiency of database systems. By leveraging the in-situ computing ability of emerging nonvolatile memory, processing-in-memory (PIM) technology shows great potential in accelerating database operations against traditional architectures without data movement overheads. In this article, we introduce ReSQM, a novel ReCAM-based accelerator, which can dramatically reduce the response time of database systems. The key novelty of ReSQM is that some commonly used database queries that would be otherwise processed inefficiently in previous studies can be in-situ accomplished with massively high parallelism by exploiting the PIM-enabled ReCAM array. ReSQM supports some typical database queries (such as SELECTION, SORT, and JOIN) effectively based on the limited computational mode of the ReCAM array. ReSQM is also equipped with a series of hardware-algorithm co-designs to maximize efficiency. We present a new data mapping mechanism that allows enjoying in-situ in-memory computations for SELECTION operating upon intermediate results. We also develop a count-based ReCAM-specific algorithm to enable the in-memory sorting without any row swapping. The relational comparisons are integrated for accelerating inequality join by making a few modifications to the ReCAM cells with negligible hardware overhead. The experimental results show that ReSQM can improve the (energy) efficiency by $611\times $ ($193\times $), $19\times $ ($17\times $), $59\times $ ($43\times $), and $307\times $ ($181\times $) in comparison to a 10-core Intel Xeon E5-2630v4 processor for SELECTION, SORT, equi-join, and inequality join, respectively. In contrast to state-of-the-art CMOS-based CAM, GPU, FPGA, NDP, and PIM solutions, ReSQM can also offer $2.2\times 39\times $ speedups. [ABSTRACT FROM AUTHOR]