46 research outputs found
Dynamic Determinacy Race Detection for Task-Parallel Programs with Promises
Much of the past work on dynamic data-race and determinacy-race detection algorithms for task parallelism has focused on structured parallelism with fork-join constructs and, more recently, with future constructs. This paper addresses the problem of dynamic detection of data-races and determinacy-races in task-parallel programs with promises, which are more general than fork-join constructs and futures. The motivation for our work is twofold. First, promises have now become a mainstream synchronization construct, with their inclusion in multiple languages, including C++, JavaScript, and Java. Second, past work on dynamic data-race and determinacy-race detection for task-parallel programs does not apply to programs with promises, thereby identifying a vital need for this work.
This paper makes multiple contributions. First, we introduce a featherweight programming language that captures the semantics of task-parallel programs with promises and provides a basis for formally defining determinacy using our semantics. This definition subsumes functional determinacy (same output for same input) and structural determinacy (same computation graph for same input). The main theoretical result shows that the absence of data races is sufficient to guarantee determinacy with both properties. We are unaware of any prior work that established this result for task-parallel programs with promises. Next, we introduce a new Dynamic Race Detector for Promises that we call DRDP. DRDP is the first known race detection algorithm that executes a task-parallel program sequentially without requiring the serial-projection property; this is a critical requirement since programs with promises do not satisfy the serial-projection property in general. Finally, the paper includes experimental results obtained from an implementation of DRDP. The results show that, with some important optimizations introduced in our work, the space and time overheads of DRDP are comparable to those of more restrictive race detection algorithms from past work. To the best of our knowledge, DRDP is the first determinacy race detector for task-parallel programs with promises
Doctor of Philosophy
dissertationHigh Performance Computing (HPC) on-node parallelism is of extreme importance to guarantee and maintain scalability across large clusters of hundreds of thousands of multicore nodes. HPC programming is dominated by the hybrid model "MPI + X", with MPI to exploit the parallelism across the nodes, and "X" as some shared memory parallel programming model to accomplish multicore parallelism across CPUs or GPUs. OpenMP has become the "X" standard de-facto in HPC to exploit the multicore architectures of modern CPUs. Data races are one of the most common and insidious of concurrent errors in shared memory programming models and OpenMP programs are not immune to them. The OpenMP-provided ease of use to parallelizing programs can often make it error-prone to data races which become hard to find in large applications with thousands lines of code. Unfortunately, prior tools are unable to impact practice owing to their poor coverage or poor scalability. In this work, we develop several new approaches for low overhead data race detection. Our approaches aim to guarantee high precision and accuracy of race checking while maintaining a low runtime and memory overhead. We present two race checkers for C/C++ OpenMP programs that target two different classes of programs. The first, ARCHER, is fast but requires large amount of memory, so it ideally targets applications that require only a small portion of the available on-node memory. On the other hand, SWORD strikes a balance between fast zero memory overhead data collection followed by offline analysis that can take a long time, but it often report most races quickly. Given that race checking was impossible for large OpenMP applications, our contributions are the best available advances in what is known to be a difficult NP-complete problem. We performed an extensive evaluation of the tools on existing OpenMP programs and HPC benchmarks. Results show that both tools guarantee to identify all the races of a program in a given run without reporting any false alarms. The tools are user-friendly, hence serve as an important instrument for the daily work of programmers to help them identify data races early during development and production testing. Furthermore, our demonstrated success on real-world applications puts these tools on the top list of debugging tools for scientists at large
Achieving High Performance and High Productivity in Next Generational Parallel Programming Languages
Processor design has turned toward parallelism and heterogeneity
cores to achieve performance and energy efficiency. Developers
find high-level languages attractive because they use abstraction
to offer productivity and portability over hardware complexities.
To achieve performance, some modern implementations of high-level
languages use work-stealing scheduling for load balancing of
dynamically created tasks. Work-stealing is a promising approach
for effectively exploiting software parallelism on parallel
hardware. A programmer who uses work-stealing explicitly
identifies potential parallelism and the runtime then schedules
work, keeping otherwise idle hardware busy while relieving
overloaded hardware of its burden.
However, work-stealing comes with substantial overheads. These
overheads arise as a necessary side effect of the implementation
and hamper parallel performance. In addition to runtime-imposed
overheads, there is a substantial cognitive load associated with
ensuring that parallel code is data-race free. This dissertation
explores the overheads associated with achieving high performance
parallelism in modern high-level languages.
My thesis is that, by exploiting existing underlying mechanisms
of managed runtimes; and by extending existing language design,
high-level languages will be able to deliver productivity and
parallel performance at the levels necessary for widespread
uptake.
The key contributions of my thesis are: 1) a detailed analysis of
the key sources of overhead associated with a work-stealing
runtime, namely sequential and dynamic overheads; 2) novel
techniques to reduce these overheads that use rich features of
managed runtimes such as the yieldpoint mechanism, on-stack
replacement, dynamic code-patching, exception handling support,
and return barriers; 3) comprehensive analysis of the resulting
benefits, which demonstrate that work-stealing overheads can be
significantly reduced, leading to substantial performance
improvements; and 4) a small set of language extensions that
achieve both high performance and high productivity with minimal
programmer effort.
A managed runtime forms the backbone of any modern implementation
of a high-level language. Managed runtimes enjoy the benefits of
a long history of research and their implementations are highly
optimized. My thesis demonstrates that converging these highly
optimized features together with the expressiveness of high-level
languages, gives further hope for achieving high performance and
high productivity on modern parallel hardwar
Efficient Race Detection with Futures
This paper addresses the problem of provably efficient and practically good
on-the-fly determinacy race detection in task parallel programs that use
futures. Prior works determinacy race detection have mostly focused on either
task parallel programs that follow a series-parallel dependence structure or
ones with unrestricted use of futures that generate arbitrary dependences. In
this work, we consider a restricted use of futures and show that it can be race
detected more efficiently than general use of futures.
Specifically, we present two algorithms: MultiBags and MultiBags+. MultiBags
targets programs that use futures in a restricted fashion and runs in time
, where is the sequential running time of the
program, is the inverse Ackermann's function, is the total number
of memory accesses, is the dynamic count of places at which parallelism is
created. Since is a very slowly growing function (upper bounded by
for all practical purposes), it can be treated as a close-to-constant overhead.
MultiBags+ an extension of MultiBags that target programs with general use of
futures. It runs in time where , ,
and are defined as before, and is the number of future operations in
the computation. We implemented both algorithms and empirically demonstrate
their efficiency
Easier Parallel Programming with Provably-Efficient Runtime Schedulers
Over the past decade processor manufacturers have pivoted from increasing uniprocessor performance to multicore architectures. However, utilizing this computational power has proved challenging for software developers. Many concurrency platforms and languages have emerged to address parallel programming challenges, yet writing correct and performant parallel code retains a reputation of being one of the hardest tasks a programmer can undertake.
This dissertation will study how runtime scheduling systems can be used to make parallel programming easier. We address the difficulty in writing parallel data structures, automatically finding shared memory bugs, and reproducing non-deterministic synchronization bugs. Each of the systems presented depends on a novel runtime system which provides strong theoretical performance guarantees and performs well in practice