In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open-source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry), providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms.
Using SIMD (Single Instruction Multiple Data) is a costeffective way to explore data parallelism on modern processors. Most processor vendors today provide SIMD engines, such as Altivec/VSX for POWER, SSE/AVX for Intel processors, and NEON for ARM. While high-level SIMD programming models are rapidly evolving, for many SIMD developers, the most effective way to get the performance out of SIMD is still by programming directly via vendorprovided SIMD intrinsics. However, intrinsics programming is both tedious and error-prone, and worst of all, introduces non-portable codes.This paper presents the Generic SIMD Library (https://github.com/genericsimd/generic simd/), an open-source, portable C++ interface that provides an abstraction of short vectors and overloads most C/C++ operators for short vectors. The library provides several mappings from platform-specific intrinsics to the generic SIMD intrinsic interface so that codes developed based on the library are portable across different SIMD platforms.We have evaluated the library with several applications from the multimedia, data analytics and math domains. Compared with platform-specific intrinsics codes, using Generic SIMD Library results in less line-of-code, a 22% reduction on average, and achieves similar performance as platform-specific intrinsics versions.
Many Big Data analytics essentially explore the relationship among interconnected entities, which are naturally represented as graphs. However, due to the irregular data access patterns in the graph computations, it remains a fundamental challenge to deliver highly efficient solutions for large scale graph analytics. Such inefficiency restricts the utilization of many graph algorithms in Big Data scenarios. To address the performance issues in large scale graph analytics, we develop a graph processing system called System G, which explores efficient graph data organization for parallel computing architectures. We discuss various graph data organizations and their impact on data locality during graph traversals, which results in various cache performance behavior on processor side. In addition, we analyze data parallelism from architecture's perspective and experimentally show the efficiency for System G based graph analytics. We present experimental results for commodity multicore clusters and IBM PERCS supercomputers to illustrate the performance of System G for large scale graph analytics.
Graph analytics on big data is currently a very active area of research in both industry and academia. To support graph analytics efficiently a large number of graph processing systems have emerged targeting various perspectives of a graph application such as in memory and on disk representations, persistent storage, database capability, runtimes and execution models for exploiting parallelism, etc.In this paper we discuss a novel graph processing system called System G Native Store which allows for efficient graph data organization and processing on modern computing architectures. In particular we describe a runtime designed to exploit multiple levels of parallelism and a generic infrastructure that allows users to express graphs with various in memory and persistent storage properties. We experimentally show the efficiency of System G Native Store for processing graph queries on state-of-the-art platforms.
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