Dynamic Data Race Detection for Structured Parallelism

With the advent of multicore processors and an increased emphasis on parallel computing, parallel programming has become a fundamental requirement for achieving available performance. Parallel programming is inherently hard because, to reason about the correctness of a parallel program, programmers have to consider large numbers of interleavings of statements in different threads in the program. Though structured parallelism imposes some restrictions on the programmer, it is an attractive approach because it provides useful guarantees such as deadlock-freedom. However, data races remain a challenging source of bugs in parallel programs. Data races may occur only in few of the possible schedules of a parallel program, thereby making them extremely hard to detect, reproduce, and correct. In the past, dynamic data race detection algorithms have suffered from at least one of the following limitations: some algorithms have a worst-case linear space and time overhead, some algorithms are dependent on a specific scheduling technique, some algorithms generate false positives and false negatives, some have no empirical evaluation as yet, and some require sequential execution of the parallel program. In this thesis, we introduce dynamic data race detection algorithms for structured parallel programs that overcome past limitations. We present a race detection algorithm called ESP-bags that requires the input program to be executed sequentially and another algorithm called SPD3 that can execute the program in parallel. While the ESP-bags algorithm addresses all the above mentioned limitations except sequential execution, the SPD3 algorithm addresses the issue of sequential execution by scaling well across highly parallel shared memory multiprocessors. Our algorithms incur constant space overhead per memory location and time overhead that is independent of the number of processors on which the programs execute. Our race detection algorithms support a rich set of parallel constructs (including async, finish, isolated, and future) that are found in languages such as HJ, X10, and Cilk. Our algorithms for async, finish, and future are precise and sound for a given input. In the presence of isolated, our algorithms are precise but not sound. Our experiments show that our algorithms (for async, finish, and isolated) perform well in practice, incurring an average slowdown of under 3x over the original execution time on a suite of 15 benchmarks. SPD3 is the first practical dynamic race detection algorithm for async-finish parallel programs that can execute the input program in parallel and use constant space per memory location. This takes us closer to our goal of building dynamic data race detectors that can be "always-on" when developing parallel applications

Dag-calculus: a calculus for parallel computation

International audienceIncreasing availability of multicore systems has led to greater focus on the design and implementation of languages for writing parallel programs. Such languages support various abstractions for parallelism, such as fork-join, async-finish, futures. While they may seem similar, these abstractions lead to different semantics, language design and implementation decisions, and can significantly impact the performance of end-user applications. In this paper, we consider the question of whether it would be possible to unify various paradigms of parallel computing. To this end, we propose a calculus, called dag calculus, that can encode fork-join, async-finish, and futures, and possibly others. We describe dag calculus and its semantics, establish translations from the afore-mentioned paradigms into dag calculus. These translations establish that dag calculus is sufficiently powerful for encoding programs written in prevailing paradigms of parallelism. We present concurrent algorithms and data structures for realizing dag calculus on multi-core hardware and prove that the proposed techniques are consistent with the semantics. Finally, we present an implementation of the calculus and evaluate it empirically by comparing its performance to highly optimized code from prior work. The results show that the calculus is expressive and that it competes well with, and sometimes outperforms, the state of the art

Communicating generators in Javascript

This paper outlines the design, performance, and use of an application programming interface and library for concurrent programming with CSP in JavaScript. The implementation harnesses ECMAScript 6 Generators to provide cooperative scheduling and channel communication within a single JavaScript engine. External channels lie atop WebSockets, amongst other web technologies, to enable multicore and distributed execution across standard web browsers and Node.js servers. Low-level benchmarks indicate that scheduling and messaging performance is within expectations for this dynamic and diverse execution environment. Sample code snippets highlight the applicability of CSP to contemporary web development in hiding the location of computation and state through the channel abstraction. The ”callback hell” scenario common to many JavaScript applications is alleviated by using channels instead of callbacks, and the possibility of performing parallel and scientific computing is explored with promising results. Finally, the limitations of the present design are discussed, and possible enhancements such as the dynamic migration of state and code are considered.peer-reviewe

Efficient Data Race Detection for Async-Finish Parallelism

Abstract. A major productivity hurdle for parallel programming is the presence of data races. Data races can lead to all kinds of harmful program behaviors, includ-ing determinism violations and corrupted memory. However, runtime overheads of current dynamic data race detectors are still prohibitively large (often incurring slowdowns of 10 × or larger) for use in mainstream software development. In this paper, we present an efficient dynamic race detector algorithm targeting the async-finish task-parallel parallel programming model. The async and finish constructs are at the core of languages such as X10 and Habanero Java (HJ). These constructs generalize the spawn-sync constructs used in Cilk, while still ensuring that all computation graphs are deadlock-free. We have implemented our algorithm in a tool called TASKCHECKER and eval-uated it on a suite of 12 benchmarks. To reduce overhead of the dynamic analysis, we have also implemented various static optimizations in the tool. Our experi-mental results indicate that our approach performs well in practice, incurring an average slowdown of 3.05 × compared to a serial execution in the optimized case.

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