Learning representations of software codes is a critical problem for a wide range of system applications, e.g., compiler optimization, software classification, malicious software detection, and performance optimization. Recently, learning graph-based representations of software programs has been used to model the inherent structural dependencies in programming languages (e.g., C++, Python). In this paper, we propose a novel graph neural network framework that utilizes multifractal analysis for LLVM intermediate representations (IR). We then show empirically that the proposed framework is capable of capturing long-range structural dependencies that appear in software codes. We conduct experiments and comparisons on two downstream system applications: (1) predicting heterogeneous compute device mappings (graph classification), and (2) compiler reachability analysis (node classification). We observe that introducing a structural inductive bias through multifractal topological features enables GNNs to capture long-range dependencies among nodes, thus, it improves the accuracy of GNN models for applications that require learning code representations.

Apache Spark is a popular framework for data analytics with attractive features such as fault tolerance and interoperability with the Hadoop ecosystem. Unfortunately, many analytics operations in Spark are an order of magnitude or more slower compared to native implementations written with high performance computing tools such as MPI. There is a need to bridge the performance gap while retaining the benefits of the Spark ecosystem such as availability, productivity, and fault tolerance. In this paper, we propose a system for integrating MPI with Spark and analyze the costs and benefits of doing so for four distributed graph and machine learning applications. We show that offloading computation to an MPI environment from within Spark provides 3.1−17.7× speedups on the four sparse applications, including all of the overheads. This opens up an avenue to reuse existing MPI libraries in Spark with little effort.

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and the Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99χ and 2062x speedups on the two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5χ respectively with 20 nodes on real datasets. We demonstrate weak scaling on a synthetic dataset with 1024 subjects, equivalent in size to the biggest fMRI dataset collected until now, on up to 1024 nodes and 32,768 cores.

Real-time functional magnetic resonance imaging (rtfMRI) is an emerging approach for studying the functioning of the human brain. Computational challenges combined with high data velocity have to this point restricted rtfMRI analyses to studying regions of the brain independently. However, given that neural processing is accomplished via functional interactions among brain regions, neuroscience could stand to benefit from rtfMRI analyses of full-brain interactions. In this paper, we extend such an offline analysis method, full correlation matrix analysis (FCMA), to enable its use in rtfMRI studies. Specifically, we introduce algorithms capable of processing real-time data for all stages of the FCMA machine learning workflow: incremental feature selection, model updating, and real-time classification. We also present an actor-model based distributed system designed to support FCMA and other rtfMRI analysis methods. Experiments show that our system successfully analyzes a stream of brain volumes and returns neurofeedback with less than 180 ms of lag. Our real-time FCMA implementation provides the same accuracy as an optimized offline FCMA toolbox while running 3.6–6.2x faster.

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.

The commoditization of big data analytics, that is, the deployment, tuning, and future development of big data processing platforms such as MapReduce, relies on a thorough understanding of relevant use cases and workloads. In this work we propose BTWorld, a use case for time-based big data analytics that is representative for processing data collected periodically from a global-scale distributed system. BTWorld enables a data-driven approach to understanding the evolution of BitTorrent, a global file-sharing network that has over 100 million users and accounts for a third of today's upstream traffic. We describe for this use case the analyst questions and the structure of a multi-terabyte data set. We design a MapReduce-based logical workflow, which includes three levels of data dependency - inter-query, inter-job, and intra-job - and a query diversity that make the BTWorld use case challenging for today's big data processing tools; the workflow can be instantiated in various ways in the MapReduce stack. Last, we instantiate this complex workflow using Pig-Hadoop-HDFS and evaluate the use case empirically. Our MapReduce use case has challenging features: small (kilobytes) to large (250 MB) data sizes per observed item, excellent (10-6) and very poor (102) selectivity, and short (seconds) to long (hours) job duration.