5,330 research outputs found

    Addressing performance requirements in the FDT-based design of distributed systems

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    The development of distributed systems is generally regarded as a complex and costly task, and for this reason formal description techniques such as LOTOS and ESTELLE (both standardized by the ISO) are increasingly used in this process. Our experience is that LOTOS can be exploited at many stages on the design trajectory, from requirements specification to implementation, but that the language elements do not allow direct formalization of performance requirements. To avoid duplication of effort by using two formalisms with distinct approaches, we propose a design method that incorporates performance constraints in an heuristic but effective manner

    Safe and Verifiable Design of Concurrent Java Programs

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    The design of concurrent programs has a reputation for being difficult, and thus potentially dangerous in safetycritical real-time and embedded systems. The recent appearance of Java, whilst cleaning up many insecure aspects of OO programming endemic in C++, suffers from a deceptively simple threads model that is an insecure variant of ideas that are over 25 years old [1]. Consequently, we cannot directly exploit a range of new CASE tools -- based upon modern developments in parallel computing theory -- that can verify and check the design of concurrent systems for a variety of dangers\ud such as deadlock and livelock that otherwise plague us during testing and maintenance and, more seriously, cause catastrophic failure in service. \ud Our approach uses recently developed Java class\ud libraries based on Hoare's Communicating Sequential Processes (CSP); the use of CSP greatly simplifies the design of concurrent systems and, in many cases, a parallel approach often significantly simplifies systems originally approached sequentially. New CSP CASE tools permit designs to be verified against formal specifications\ud and checked for deadlock and livelock. Below we introduce CSP and its implementation in Java and develop a small concurrent application. The formal CSP description of the application is provided, as well as that of an equivalent sequential version. FDR is used to verify the correctness of both implementations, their\ud equivalence, and their freedom from deadlock and livelock

    Structure-Aware Dynamic Scheduler for Parallel Machine Learning

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    Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates. A natural solution is to turn to distributed computing on a cluster; however, naive, unstructured parallelization of ML algorithms does not usually lead to a proportional speedup and can even result in divergence, because dependencies between model elements can attenuate the computational gains from parallelization and compromise correctness of inference. Recent efforts toward this issue have benefited from exploiting the static, a priori block structures residing in ML algorithms. In this paper, we take this path further by exploring the dynamic block structures and workloads therein present during ML program execution, which offers new opportunities for improving convergence, correctness, and load balancing in distributed ML. We propose and showcase a general-purpose scheduler, STRADS, for coordinating distributed updates in ML algorithms, which harnesses the aforementioned opportunities in a systematic way. We provide theoretical guarantees for our scheduler, and demonstrate its efficacy versus static block structures on Lasso and Matrix Factorization

    DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

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    We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).Comment: LDAV 2018, October 201

    PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation

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    Online aggregation provides estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution. This allows for the interactive data exploration of the largest datasets. In this paper we introduce the first framework for parallel online aggregation in which the estimation virtually does not incur any overhead on top of the actual execution. We define a generic interface to express any estimation model that abstracts completely the execution details. We design a novel estimator specifically targeted at parallel online aggregation. When executed by the framework over a massive 8TB8\text{TB} TPC-H instance, the estimator provides accurate confidence bounds early in the execution even when the cardinality of the final result is seven orders of magnitude smaller than the dataset size and without incurring overhead.Comment: 36 page

    Non-Strict Independence-Based Program Parallelization Using Sharing and Freeness Information.

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    The current ubiquity of multi-core processors has brought renewed interest in program parallelization. Logic programs allow studying the parallelization of programs with complex, dynamic data structures with (declarative) pointers in a comparatively simple semantic setting. In this context, automatic parallelizers which exploit and-parallelism rely on notions of independence in order to ensure certain efficiency properties. “Non-strict” independence is a more relaxed notion than the traditional notion of “strict” independence which still ensures the relevant efficiency properties and can allow considerable more parallelism. Non-strict independence cannot be determined solely at run-time (“a priori”) and thus global analysis is a requirement. However, extracting non-strict independence information from available analyses and domains is non-trivial. This paper provides on one hand an extended presentation of our classic techniques for compile-time detection of non-strict independence based on extracting information from (abstract interpretation-based) analyses using the now well understood and popular Sharing + Freeness domain. This includes algorithms for combined compile-time/run-time detection which involve special run-time checks for this type of parallelism. In addition, we propose herein novel annotation (parallelization) algorithms, URLP and CRLP, which are specially suited to non-strict independence. We also propose new ways of using the Sharing + Freeness information to optimize how the run-time environments of goals are kept apart during parallel execution. Finally, we also describe the implementation of these techniques in our parallelizing compiler and recall some early performance results. We provide as well an extended description of our pictorial representation of sharing and freeness information
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