Programming Heterogeneous Parallel Machines Using Refactoring and Monte-Carlo Tree Search

Funding: This work was supported by the EU Horizon 2020 project, TeamPlay, Grant Number 779882, and UK EPSRC Discovery, Grant Number EP/P020631/1.This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree search for deriving mappings of tasks to available hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scalable speedups on a number of case studies showing speedups of up to 41 over the sequential code on a 24-core machine with one GPU. We also demonstrate that the speedups obtained by mappings derived by the MCTS algorithm are within 5–15% of the best-obtained manual parallelisation.Publisher PDFPeer reviewe

Mapping Pipeline Skeletons onto Heterogeneous Platforms

Mapping applications onto parallel platforms is a challenging problem, that becomes even more difficult when platforms are heterogeneous –nowadays a standard assumption. A high-level approach to parallel programming not only eases the application developer’s task, but it also provides additional information which can help realize an efficient mapping of the application. In this paper, we discuss the mapping of pipeline skeletons onto different types of platforms: Fully Homogeneous platforms with identical processors and interconnection links; Communication Homogeneous platforms, with identical links but different speed processors; and finally, Fully Heterogeneous platforms. We assume that a pipeline stage must be mapped on a single processor, and we establish new theoretical complexity results for different mapping policies: a mapping can be required to be one-to-one (a processor is assigned at most one stage), or interval-based (a processor is assigned an interval of consecutive stages), or fully general. In particular, we show taht determining the optimal interval-based mapping is NP-hard for Communication Homogeneous platfors, and this result assesses the complexity of the well-known chains-to-chains problem for different-speed processors. We provide several efficient polynomial heuristics for the most important policy/platform combination, namely interval-based mappings on Communication Homogeneous platforms. These heuristics are compared to the optimal result provided by the formulation of the problem in terms of the solution of an integer linear program, for small problem instances

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Throughput optimization for pipeline workflow scheduling with setup time

Large-scale data-driven financial risk assessment

The state of data in finance makes near real-time and consistent assessment of financial risks almost impossible today. The aggregate measures produced by traditional methods are rigid, infrequent, and not available when needed. In this chapter, we make the point that this situation can be remedied by introducing a suitable standard for data and algorithms at the deep technological level combined with the use of Big Data technologies. Specifically, we present the ACTUS approach to standardizing the modeling of financial contracts in view of financial analysis, which provides a methodological concept together with a data standard and computational algorithms. We present a proof of concept of ACTUS-based financial analysis with real data provided by the European Central Bank. Our experimental results with respect to computational performance of this approach in an Apache Spark based Big Data environment show close to linear scalability. The chapter closes with implications for data science