A Case Study in Coordination Programming: Performance Evaluation of S-Net vs Intel's Concurrent Collections

We present a programming methodology and runtime performance case study comparing the declarative data flow coordination language S-Net with Intel's Concurrent Collections (CnC). As a coordination language S-Net achieves a near-complete separation of concerns between sequential software components implemented in a separate algorithmic language and their parallel orchestration in an asynchronous data flow streaming network. We investigate the merits of S-Net and CnC with the help of a relevant and non-trivial linear algebra problem: tiled Cholesky decomposition. We describe two alternative S-Net implementations of tiled Cholesky factorization and compare them with two CnC implementations, one with explicit performance tuning and one without, that have previously been used to illustrate Intel CnC. Our experiments on a 48-core machine demonstrate that S-Net manages to outperform CnC on this problem.Comment: 9 pages, 8 figures, 1 table, accepted for PLC 2014 worksho

S+Net: extending functional coordination with extra-functional semantics

This technical report introduces S+Net, a compositional coordination language for streaming networks with extra-functional semantics. Compositionality simplifies the specification of complex parallel and distributed applications; extra-functional semantics allow the application designer to reason about and control resource usage, performance and fault handling. The key feature of S+Net is that functional and extra-functional semantics are defined orthogonally from each other. S+Net can be seen as a simultaneous simplification and extension of the existing coordination language S-Net, that gives control of extra-functional behavior to the S-Net programmer. S+Net can also be seen as a transitional research step between S-Net and AstraKahn, another coordination language currently being designed at the University of Hertfordshire. In contrast with AstraKahn which constitutes a re-design from the ground up, S+Net preserves the basic operational semantics of S-Net and thus provides an incremental introduction of extra-functional control in an existing language.Comment: 34 pages, 11 figures, 3 table

Parallel Processing of Image Segmentation Data Using Hadoop

The use of sequential programming is slowly getting replaced by distributed and parallel computing which is widely being used in computing industries to handle tasks with big data and various high-end computing applications comprising of huge image and video data banks. Moreover, image processing using parallel computation is also gaining momentum in today's technological era. Nowadays researchers are coming up with various methodologies to tackle high scale image processing applications by implementing parallel computing methodologies to carry out the specified image processing application task and simultaneously checking its performance against sequential programming. At the same time there are constraints on what can be done to maximize the task performance using high end multi-core CPU's with advanced buses and interconnects that offer high bandwidth with low system latency. It is to be noted that there is no availability of standardized image processing task which can be used to evaluate a single node system. In this paper, we propose an efficient parallel processing algorithm to perform the task of image segmentation with the foremost aim to analyze the threshold of data size at which the proposed method outperforms sequential programming method in terms of task execution time by analyzing the distribution of average CPU cores usage and its threads over the execution time. The proposed methodology could be useful for researchers, as it can perform multiple image segmentation in parallel, which can save a lot of time of the user. For the purpose of comparison, we also implemented the same image segmentation task using sequential method of programming in an integrated development environment platform

The Cost and Benefits of Coordination Programming: Two Case Studies in Concurrent Collection and S-Net

Electronic version of an article published as Pavel Zaichenkov et al, Parallel Processing Letters, Vol. 26 (3), 2016, 24 pages. DOI: http://www.worldscientific.com/doi/abs/10.1142/S0129626416500110 © 2016 World Scientific Publishing Company http://www.worldscientific.com/worldscinet/pplThis is an evaluation study of the expressiveness provided and the performance delivered by the coordination language S-NET in comparison to Intel’s Concurrent Collections (CnC). An S-NET application is a network of black-box compute components connected through anonymous data streams, with the standard input and output streams linking the application to the environment. Our case study is based on two applications: a face detection algorithm implemented as a pipeline of feature classifiers and a numerical algorithm from the linear algebra domain, namely Cholesky decomposition. The selected applications are representative and have been selected by Intel researchers as evaluation testbeds for CnC in the past. We implement various versions of both algorithms in S-NET and compare them with equivalent CnC implementations, both with and without tuning, previously published by the CnC community. Our experiments on a large-scale server system demonstrate that S-Net delivers very similar scalability and absolute performance on the studied examples as tuned CnC codes do, even without specific tuning. At the same time, S-Net does achieve a much more complete separation of concerns between compute and coordination layers than CnC even intends to.Peer reviewedFinal Accepted Versio

Modeling Streams-based Variants of Ant Colony Optimisation for Parallel Systems

Wei Cheng, Frank Penczek, Clemens Grelck, Raimund Kirner, Bernd Scheuermann, Alex Shafarenko, 'Modeling Streams-based Variants of Ant Colony Optimisation for Parallel Systems' in Proceedings: 2nd HiPEAC Workshop on Feedback-Directed Compiler Optimization for Multi-Core Architectures. Berlin, Germany. 22 January 2013In this paper we present the implementation of a concurrent ant colony optimisation based solver for the combinatorial Single Machine Total Weighted Tardiness Problem (ACO- SMTWTP). We introduce S-Net, a coordination language based on dataflow principles, report on the performance of the implementation and compare it against a sequential and a parallel implementation of the same algorithm in C. As the workload of the optimisation algorithm is highly irregu- lar we consider this application to be an important use-case for runtime measurement directed optimisations of the co- ordination rogram as much as for guiding optimisations of numerical code

Statistical Performance Analysis of an Ant-Colony Optimisation Application in S-NET

Kenneth MacKenzie, Philip K. F. Hölzenspies, Kevin Hammond, Raimund Kirner, Vu Thien Nga Nguyen, Iraneus te Boekhorst, Clemens Grelck, Raphael Poss, Merijn Verstraaten, 'Statistical Performance Analysis of an Ant-Colony Optimisation Application in S-NET'. Paper presented at the 2nd Workshop on Feedback-Directed Compiler Optimization for Multi-Core Architectures. Berlin, Germany, 12 January 2013.We consider an ant-colony optimsation problem implemented on a multicore system as a collection of asynchronous stream- processing components under the control of the S-NET coordina- tion language. Statistical analysis and visualisation techniques are used to study the behaviour of the application, and this enables us to discover and correct problems in both the application program and the run-time system underlying S-NET

The TeamPlay project : analysing and optimising time, energy, and security for cyber-physical systems

Funding: This work was supported by the EU Horizon-2020 project TeamPlay (https://www.teamplay-h2020.eu), grant #779882.Non-functional properties, such as energy, time, and security (ETS) are becoming increasingly important for the programming of Cyber-Physical Systems (CPS). This paper describes TeamPlay, a research project funded under the EU Horizon 2020 programme between January 2018 and June 2021.TeamPlay aimed to provide the system designer with a toolchain for developing embedded applications where ETS properties are first-class citizens, allowing the developer to reflect directly on energy, time and security properties at the source code level. In this paper we give an overview of the TeamPlay methodology, introduce the challenges and solutions of our approach and summarise the results achieved. Overall, applying our TeamPlay methodology led to an improvement of up to 18% performance and 52% energy usage over traditional approaches.Postprin

Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study

A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.Additional co-authors: Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Vis