The parallel event loop model and runtime: a parallel programming model and runtime system for safe event-based parallel programming

Recent trends in programming models for server-side development have shown an increasing popularity of event-based single- threaded programming models based on the combination of dynamic languages such as JavaScript and event-based runtime systems for asynchronous I/O management such as Node.JS. Reasons for the success of such models are the simplicity of the single-threaded event-based programming model as well as the growing popularity of the Cloud as a deployment platform for Web applications. Unfortunately, the popularity of single-threaded models comes at the price of performance and scalability, as single-threaded event-based models present limitations when parallel processing is needed, and traditional approaches to concurrency such as threads and locks don't play well with event-based systems. This dissertation proposes a programming model and a runtime system to overcome such limitations by enabling single-threaded event-based applications with support for speculative parallel execution. The model, called Parallel Event Loop, has the goal of bringing parallel execution to the domain of single-threaded event-based programming without relaxing the main characteristics of the single-threaded model, and therefore providing developers with the impression of a safe, single-threaded, runtime. Rather than supporting only pure single-threaded programming, however, the parallel event loop can also be used to derive safe, high-level, parallel programming models characterized by a strong compatibility with single-threaded runtimes. We describe three distinct implementations of speculative runtimes enabling the parallel execution of event-based applications. The first implementation we describe is a pessimistic runtime system based on locks to implement speculative parallelization. The second and the third implementations are based on two distinct optimistic runtimes using software transactional memory. Each of the implementations supports the parallelization of applications written using an asynchronous single-threaded programming style, and each of them enables applications to benefit from parallel execution

The parallel event loop model and runtime: a parallel programming model and runtime system for safe event-based parallel programming

Recent trends in programming models for server-side development have shown an increasing popularity of event-based single- threaded programming models based on the combination of dynamic languages such as JavaScript and event-based runtime systems for asynchronous I/O management such as Node.JS. Reasons for the success of such models are the simplicity of the single-threaded event-based programming model as well as the growing popularity of the Cloud as a deployment platform for Web applications. Unfortunately, the popularity of single-threaded models comes at the price of performance and scalability, as single-threaded event-based models present limitations when parallel processing is needed, and traditional approaches to concurrency such as threads and locks don't play well with event-based systems. This dissertation proposes a programming model and a runtime system to overcome such limitations by enabling single-threaded event-based applications with support for speculative parallel execution. The model, called Parallel Event Loop, has the goal of bringing parallel execution to the domain of single-threaded event-based programming without relaxing the main characteristics of the single-threaded model, and therefore providing developers with the impression of a safe, single-threaded, runtime. Rather than supporting only pure single-threaded programming, however, the parallel event loop can also be used to derive safe, high-level, parallel programming models characterized by a strong compatibility with single-threaded runtimes. We describe three distinct implementations of speculative runtimes enabling the parallel execution of event-based applications. The first implementation we describe is a pessimistic runtime system based on locks to implement speculative parallelization. The second and the third implementations are based on two distinct optimistic runtimes using software transactional memory. Each of the implementations supports the parallelization of applications written using an asynchronous single-threaded programming style, and each of them enables applications to benefit from parallel execution

Parallel programming using functional languages

It has been argued for many years that functional programs are well suited to parallel evaluation. This thesis investigates this claim from a programming perspective; that is, it investigates parallel programming using functional languages. The approach taken has been to determine the minimum programming which is necessary in order to write efficient parallel programs. This has been attempted without the aid of clever compile-time analyses. It is argued that parallel evaluation should be explicitly expressed, by the programmer, in programs. To do achieve this a lazy functional language is extended with parallel and sequential combinators. The mathematical nature of functional languages means that programs can be formally derived by program transformation. To date, most work on program derivation has concerned sequential programs. In this thesis Squigol has been used to derive three parallel algorithms. Squigol is a functional calculus from program derivation, which is becoming increasingly popular. It is shown that some aspects of Squigol are suitable for parallel program derivation, while others aspects are specifically orientated towards sequential algorithm derivation. In order to write efficient parallel programs, parallelism must be controlled. Parallelism must be controlled in order to limit storage usage, the number of tasks and the minimum size of tasks. In particular over-eager evaluation or generating excessive numbers of tasks can consume too much storage. Also, tasks can be too small to be worth evaluating in parallel. Several program techniques for parallelism control were tried. These were compared with a run-time system heuristic for parallelism control. It was discovered that the best control was effected by a combination of run-time system and programmer control of parallelism. One of the problems with parallel programming using functional languages is that non-deterministic algorithms cannot be expressed. A bag (multiset) data type is proposed to allow a limited form of non-determinism to be expressed. Bags can be given a non-deterministic parallel implementation. However, providing the operations used to combine bag elements are associative and commutative, the result of bag operations will be deterministic. The onus is on the programmer to prove this, but usually this is not difficult. Also bags' insensitivity to ordering means that more transformations are directly applicable than if, say, lists were used instead. It is necessary to be able to reason about and measure the performance of parallel programs. For example, sometimes algorithms which seem intuitively to be good parallel ones, are not. For some higher order functions it is posible to devise parameterised formulae describing their performance. This is done for divide and conquer functions, which enables constraints to be formulated which guarantee that they have a good performance. Pipelined parallelism is difficult to analyse. Therefore a formal semantics for calculating the performance of pipelined programs is devised. This is used to analyse the performance of a pipelined Quicksort. By treating the performance semantics as a set of transformation rules, the simulation of parallel programs may be achieved by transforming programs. Some parallel programs perform poorly due to programming errors. A pragmatic method of debugging such programming errors is illustrated by some examples

Using a functional language and graph reduction to program multiprocessor machines or functional control of imperative programs

Journal ArticleThis paper describes an effective means for programming shared memory multiprocessors whereby a set of sequential activities are linked together for execution in parallel. The glue for this linkage is provided by a functional language implemented via graph reduction and demand evaluation. The full power of functional programming is used to obtain succinct, high level specifications of parallel computations. The imperative procedures that constitute the sequential activities facilitate efficient utilization of individual processing elements, while the mechanisms inherent in graph reduction synchronize and schedule these activities. The main contributions of this paper are: 1) an evaluation of the performance implications of parallel graph reduction; 2) a demonstration that the mechanisms of graph reduction can obtain multiprocessor performance uniformly surpassing the best uni-processor implementation of sequential algorithms running on a single node of the same machine, and 3) an illustration of our method used to program a real world fluid flow simulation problem

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

The Mind-Body Problem in the Origin of Logical Empiricism: Herbert Feigl and Psychophysical Parallelism

In the 19th century, "Psychophysical Parallelism" was the most popular solution of the mind-body problem among physiologists, psychologists and philosophers. (This is not to be mixed up with Leibnizian and other cases of "Cartesian" parallelism.) The fate of this non-Cartesian view, as founded by Gustav Theodor Fechner, is reviewed. It is shown that Feigl's "identity theory" eventually goes back to Alois Riehl who promoted a hybrid version of psychophysical parallelism and Kantian mind-body theory which was taken up by Feigl's teacher Moritz Schlick.

Architectural support for task dependence management with flexible software scheduling

The growing complexity of multi-core architectures has motivated a wide range of software mechanisms to improve the orchestration of parallel executions. Task parallelism has become a very attractive approach thanks to its programmability, portability and potential for optimizations. However, with the expected increase in core counts, finer-grained tasking will be required to exploit the available parallelism, which will increase the overheads introduced by the runtime system. This work presents Task Dependence Manager (TDM), a hardware/software co-designed mechanism to mitigate runtime system overheads. TDM introduces a hardware unit, denoted Dependence Management Unit (DMU), and minimal ISA extensions that allow the runtime system to offload costly dependence tracking operations to the DMU and to still perform task scheduling in software. With lower hardware cost, TDM outperforms hardware-based solutions and enhances the flexibility, adaptability and composability of the system. Results show that TDM improves performance by 12.3% and reduces EDP by 20.4% on average with respect to a software runtime system. Compared to a runtime system fully implemented in hardware, TDM achieves an average speedup of 4.2% with 7.3x less area requirements and significant EDP reductions. In addition, five different software schedulers are evaluated with TDM, illustrating its flexibility and performance gains.This work has been supported by the RoMoL ERC Advanced Grant (GA 321253), by the European HiPEAC Network of Excellence, by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P, TIN2016-76635-C2-2-R and TIN2016-81840-REDT), by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 671697 and No. 671610. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047.Peer ReviewedPostprint (author's final draft