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

Compiler-Driven Software Speculation for Thread-Level Parallelism

Current parallelizing compilers can tackle applications exercising regular access patterns on arrays or affine indices, where data dependencies can be expressed in a linear form. Unfortunately, there are cases that independence between statements of code cannot be guaranteed and thus the compiler conservatively produces sequential code. Programs that involve extensive pointer use, irregular access patterns, and loops with unknown number of iterations are examples of such cases. This limits the extraction of parallelism in cases where dependencies are rarely or never triggered at runtime. Speculative parallelism refers to methods employed during program execution that aim to produce a valid parallel execution schedule for programs immune to static parallelization. The motivation for this article is to review recent developments in the area of compiler-driven software speculation for thread-level parallelism and how they came about. The article is divided into two parts. In the first part the fundamentals of speculative parallelization for thread-level parallelism are explained along with a design choice categorization for implementing such systems. Design choices include the ways speculative data is handled, how data dependence violations are detected and resolved, how the correct data are made visible to other threads, or how speculative threads are scheduled. The second part is structured around those design choices providing the advances and trends in the literature with reference to key developments in the area. Although the focus of the article is in software speculative parallelization, a section is dedicated for providing the interested reader with pointers and references for exploring similar topics such as hardware thread-level speculation, transactional memory, and automatic parallelization

Performance analysis and tuning in multicore environments

Performance analysis is the task of monitor the behavior of a program execution. The main goal is to find out the possible adjustments that might be done in order improve the performance. To be able to get that improvement it is necessary to find the different causes of overhead. Nowadays we are already in the multicore era, but there is a gap between the level of development of the two main divisions of multicore technology (hardware and software). When we talk about multicore we are also speaking of shared memory systems, on this master thesis we talk about the issues involved on the performance analysis and tuning of applications running specifically in a shared Memory system. We move one step ahead to take the performance analysis to another level by analyzing the applications structure and patterns. We also present some tools specifically addressed to the performance analysis of OpenMP multithread application. At the end we present the results of some experiments performed with a set of OpenMP scientific application.Análisis de rendimiento es el área de estudio encargada de monitorizar el comportamiento de la ejecución de programas informáticos. El principal objetivo es encontrar los posibles ajustes que serán necesarios para mejorar el rendimiento. Para poder obtener esa mejora es necesario encontrar las principales causas de overhead. Actualmente estamos sumergidos en la era multicore, pero existe una brecha entre el nivel de desarrollo de sus dos principales divisiones (hardware y software). Cuando hablamos de multicore también estamos hablando de sistemas de memoria compartida. Nosotros damos un paso más al abordar el análisis de rendimiento a otro nivel por medio del estudio de la estructura de las aplicaciones y sus patrones. También presentamos herramientas de análisis de aplicaciones que son específicas para el análisis de rendimiento de aplicaciones paralelas desarrolladas con OpenMP. Al final presentamos los resultados de algunos experimentos realizados con un grupo de aplicaciones científicas desarrolladas bajo este modelo de programación.L'Anàlisi de rendiment és l'àrea d'estudi encarregada de monitorar el comportament de l'execució de programes informàtics. El principal objectiu és trobar els possibles ajustaments que seran necessaris per a millorar el rendiment. Per a poder obtenir aquesta millora és necessari trobar les principals causes de l'overhead (excessos de computació no productiva). Actualment estem immersos en l'era multicore, però existeix una rasa entre el nivell de desenvolupament de les seves dues principals divisions (maquinari i programari). Quan parlam de multicore, també estem parlant de sistemes de memòria compartida. Nosaltres donem un pas més per a abordar l'anàlisi de rendiment en un altre nivell per mitjà de l'estudi de l'estructura de les aplicacions i els seus patrons. També presentem eines d'anàlisis d'aplicacions que són específiques per a l'anàlisi de rendiment d'aplicacions paral·leles desenvolupades amb OpenMP. Al final, presentem els resultats d'alguns experiments realitzats amb un grup d'aplicacions científiques desenvolupades sota aquest model de programació

Transactional Tasks: Parallelism in Software Transactions

Many programming languages, such as Clojure, Scala, and Haskell, support different concurrency models. In practice these models are often combined, however the semantics of the combinations are not always well-defined. In this paper, we study the combination of futures and Software Transactional Memory. Currently, futures created within a transaction cannot access the transactional state safely, violating the serializability of the transactions and leading to undesired behavior. We define transactional tasks: a construct that allows futures to be created in transactions. Transactional tasks allow the parallelism in a transaction to be exploited, while providing safe access to the state of their encapsulating transaction. We show that transactional tasks have several useful properties: they are coordinated, they maintain serializability, and they do not introduce non-determinism. As such, transactional tasks combine futures and Software Transactional Memory, allowing the potential parallelism of a program to be fully exploited, while preserving the properties of the separate models where possible