SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting

Subgraph counting aims to count occurrences of a template T in a given network G(V, E). It is a powerful graph analysis tool and has found real-world applications in diverse domains. Scaling subgraph counting problems is known to be memory bounded and computationally challenging with exponential complexity. Although scalable parallel algorithms are known for several graph problems such as Triangle Counting and PageRank, this is not common for counting complex subgraphs. Here we address this challenge and study connected acyclic graphs or trees. We propose a novel vectorized subgraph counting algorithm, named Subgraph2Vec, as well as both shared memory and distributed implementations: 1) reducing algorithmic complexity by minimizing neighbor traversal; 2) achieving a highly-vectorized implementation upon linear algebra kernels to significantly improve performance and hardware utilization. 3) Subgraph2Vec improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x on a single node. 4) Subgraph2Vec in distributed mode can scale up the template size to 20 and maintain good strong scalability. 5) enabling portability to both CPU and GPU.Comment: arXiv admin note: text overlap with arXiv:1903.0439

Dynamic re-optimization techniques for stream processing engines and object stores

Large scale data storage and processing systems are strongly motivated by the need to store and analyze massive datasets. The complexity of a large class of these systems is rooted in their distributed nature, extreme scale, need for real-time response, and streaming nature. The use of these systems on multi-tenant, cloud environments with potential resource interference necessitates fine-grained monitoring and control. In this dissertation, we present efficient, dynamic techniques for re-optimizing stream-processing systems and transactional object-storage systems.^ In the context of stream-processing systems, we present VAYU, a per-topology controller. VAYU uses novel methods and protocols for dynamic, network-aware tuple-routing in the dataflow. We show that the feedback-driven controller in VAYU helps achieve high pipeline throughput over long execution periods, as it dynamically detects and diagnoses any pipeline-bottlenecks. We present novel heuristics to optimize overlays for group communication operations in the streaming model.^ In the context of object-storage systems, we present M-Lock, a novel lock-localization service for distributed transaction protocols on scale-out object stores to increase transaction throughput. Lock localization refers to dynamic migration and partitioning of locks across nodes in the scale-out store to reduce cross-partition acquisition of locks. The service leverages the observed object-access patterns to achieve lock-clustering and deliver high performance. We also present TransMR, a framework that uses distributed, transactional object stores to orchestrate and execute asynchronous components in amorphous data-parallel applications on scale-out architectures

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM

GraphflowDB: Scalable Query Processing on Graph-Structured Relations

Finding patterns over graph-structured datasets is ubiquitous and integral to a wide range of analytical applications, e.g., recommendation and fraud detection. When expressed in the high-level query languages of database management systems (DBMSs), these patterns correspond to many-to-many join computations, which generate very large intermediate relations during query processing and degrade the performance of existing systems. This thesis argues that modern query processors need to adopt two novel techniques to be efficient on growing many-to-many joins: (i) worst-case optimal join algorithms; and (ii) factorized representations. Traditional query processors generate join plans that use binary joins, which in iteration take two relations, base or intermediate, to join and produce a new relation. The theory of worst-case optimal joins have shown that this style of join processing can be provably suboptimal and hence generate unnecessarily large intermediate results. This can be avoided on cyclic join queries if the join is performed in a multi-way fashion a join-attribute-at-a-time. As its first contribution, this thesis proposes the design and implementation of a query processor and optimizer that can generate plans that mix worst-case optimal joins, i.e., attribute-at-a-time joins and binary joins, i.e., table-at-a-time joins. In contrast to prior approaches with novel join optimizers that require solving hard computational problems, such as computing low-width hypertree decompositions of queries, our join optimizer is cost-based and uses a traditional dynamic programming approach with a new cost metric. On acyclic queries, or acyclic parts of queries, sometimes the generation of large intermediate results cannot be avoided. Yet, the theory of factorization has shown that often such intermediate results can be highly compressible if they contain multi-valued dependencies between join attributes. Factorization proposes two relation representation schemes, called f- and d-representations, to represent the large intermediate results generated under many-to-many joins in a compressed format. Existing proposals to adopt factorized representations require designing processing on fully materialized general tries and novel operators that operate on entire tries, which are not easy to adopt in existing systems. As a second contribution, we describe the implementation of a novel query processing approach we call factorized vector execution that adopts f-representations. Factorized vector execution extends the traditional vectorized query processors to use multiple blocks of vectors instead of a single block allowing us to factorize intermediate results and delay or even avoid Cartesian products. Importantly, our design ensures that every core operator in the system still performs computations on vectors. As a third contribution, we further describe how to extend our factorized vector execution model with novel operators to adopt d-representations, which extend f-representations with cached and reused sub-relations. Our design here is based on using nested hash tables that can point to sub-relations instead of copying them and on directed acyclic graph-based query plans. All of our techniques are implemented in the GraphflowDB system, which was developed throughout the years to facilitate the research in this thesis. We demonstrate that GraphflowDB’s query processor can outperform existing approaches and systems by orders of magnitude on both micro-benchmarks and end-to-end benchmarks. The designs proposed in this thesis adopt common-wisdom query processing techniques of pipelining, vector-based execution, and morsel-driven parallelism to ensure easy adoption in existing systems. We believe the design can serve as a blueprint for how to adopt these techniques in existing DBMSs to make them more efficient on workloads with many-to-many joins

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Programming models, compilers, and runtime systems for accelerator computing

Accelerators, such as GPUs and Intel Xeon Phis, have become the workhorses of high-performance computing. Typically, the accelerators act as co-processors, with discrete memory spaces. They possess massive parallelism, along with many other unique architectural features. In order to obtain high performance, these features must be carefully exploited, which requires high programmer expertise. This thesis presents new programming models, and the necessary compiler and runtime systems to ease the accelerator programming process, while obtaining high performance

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Models and Algorithms for Persistent Queries over Streaming Graphs

It is natural to model and represent interaction data as graphs in a broad range of domains such as online social networks, protein interaction data, and e-commerce applications. A number of emerging applications require continuous processing and querying of interaction data that evolves at a high rate, in near real-time, which can be modelled as a streaming graph. Persistent queries, where queries are registered into the system and new results are generated incrementally as the graph edges arrive, facilitate online analysis and real-time monitoring over streaming data. Processing persistent queries over streaming graphs combines two seemingly different but challenging problems: graph querying and streaming processing. Existing systems fail to support these workloads due to (i) the complexity of graph queries that feature recursive path navigations, subgraph patterns, and path manipulation, and (ii) the unboundedness and growth rate of streaming graphs that make it infeasible to employ batch algorithms. Consequently, a growing number of applications rely on specialized solutions tailored to specific application needs. This thesis introduces foundational techniques for efficient processing of persistent queries over streaming graphs to support this emerging class of applications in a principled manner. The main contribution of this thesis is the design and development of a general-purpose streaming graph query processing framework. The novel challenges of persistent queries over streaming graphs dictate rethinking the components of the well-established query processor architecture, and this thesis introduces the models and algorithms to address these challenges uniformly. The central notion of Streaming Graph Query precisely characterizes the semantics of persistent queries over streaming graphs, making it possible to reason about the expressiveness and the complexity of queries targeted by the aforementioned applications. Streaming Graph Algebra, defined as a closure of a set of operators over streaming graphs, provides the primitive building blocks for evaluating and optimizing streaming graph queries. Efficient, incremental algorithms as the physical implementations of streaming graph algebra operators are provided, enabling streaming graph queries to be evaluated in a data-driven fashion. It is shown that the proposed algebra constitutes the foundational tool for the cost-based optimization of streaming graph queries by providing an algebraic basis for query evaluation. Overall, this thesis provides principled solutions to fundamental challenges for efficient querying of streaming graphs and describes the design and implementation of a general-purpose streaming graph query processing framework