Parallel hardware is today’s reality and language extensions that ease exploiting its promised performance flourish. For most mainstream languages, one or more tailored solutions exist that address the specific needs of the language to access parallel hardware. Yet, one widely used language is still stuck in the sequential past: JavaScript, the lingua franca of the web.

Our position is that existing solutions do not transfer well to the world of JavaScript due to differences in programming models, the additional requirements of the web, like safety, and to developer expectations. To address this we propose River Trail, a new parallel programming API designed specifically for JavaScript and we show how it satisfies the needs of the web. To prove that our approach is viable, we have implemented a prototype JIT compiler in Firefox that shows an order of magnitude performance improvement for a realistic web application.

High-performance computing has been on an inexorable march from gigascale to tera- and petascale, with many researchers now actively contemplating exascale (1018, or a million trillion operations per second) systems. This progression is being accelerated by the rapid increase in multi- and many-core processors, which allow even greater opportunities for parallelism. Such densities, though, give rise to a new cohort of challenges; for example, containing system software overhead, dealing with large numbers of schedulable entities, and maintaining energy efficiency.

We are studying software and processor-architectural features that will allow us to achieve these goals. We believe that exascale operation will require significant “out of the box” thinking, specifically in terms of the role of operating systems and system software. We describe some of our research into how these goals can be achieved.

This paper presents a lightweight task framework and accompanying type system that statically guarantee deterministic execution. The framework is based on the familiar model of fork-join parallelism, but with two important twists. First, child tasks do not begin execution immediately upon creation, but rather they are both scheduled and joined as one atomic action; this change prevents the parent task from racing with its children. Second, the body of a child task is specified as a parallel closure.

Parallel closures are a novel variation on traditional closures in which the data inherited from the environment is read-only. Parallel closures have the important property that they can be executed in parallel with one another without creating data races, even if they share the same environment. We also have a controlled means to grant mutable access to data in the environment where necessary.

We have implemented a prototype of our framework in Java. The prototype includes a typechecker that enforces the constraint that parallel closures cannot modify their environment. The paper describes how the prototype has been used to implement a number of realistic examples and also explains how parallel closures can support the creation of structured parallel programming abstractions.

Concurrent programming has become ubiquitous, but today’s widely-used concurrent programming models provide few safety guarantees, making it easy to write code with subtle errors. Models that do give strong guarantees often can only express a relatively limited class of programs. We argue that a concurrent programming model should offer strong safety guarantees, while still providing the flexibility and performance needed to support the many ways that concurrency is used in complex, interactive applications.

To achieve this, we propose a new programming model based on tasks with effects. In this model, the core unit of work is a dynamically-created task. The key feature of our model is that each task has programmer-specified, statically-checked effects, and a runtime scheduler is used to ensure that two tasks are run concurrently only if their effects are non-interfering. Our model guarantees strong safety properties, including data race freedom and a form of atomicity. We describe this programming model and its properties, and propose several research questions related to it.

We present a virtual machine prototype, called HydraVM, that automatically extracts parallelism from legacy sequential code (at the bytecode level) through a set of techniques including code profiling, data dependency analysis, and execution analysis. HydraVM is built by extending the Jikes RVM and modifying its baseline compiler, and exploits software transactional memory to manage concurrent and out-of-order memory accesses. Our experimental studies show up to 5 speedup on the JOlden benchmark.

General purpose GPU (GPGPU) programming frameworks such as OpenCL and CUDA allow running individual computation kernels sequentially on a device. However, in some cases it is possible to utilize device resources more efficiently by running kernels concurrently. This raises questions about load balancing and resource allocation that have not previously warranted investigation. For example, what kernel characteristics impact the optimal partitioning of resources among concurrently executing kernels? Current frameworks do not provide the ability to easily run kernels concurrently with fine-grained and dynamic control over resource partitioning. We present KernelMerge, a kernel scheduler that runs two OpenCL kernels concurrently on one device. KernelMerge furnishes a number of settings that can be used to survey concurrent or single kernel configurations, and to investigate how kernels interact and influence each other, or themselves. KernelMerge provides a concurrent kernel scheduler compatible with the OpenCL API.

We present an argument on the benefits of running kernels concurrently. We demonstrate how to use KernelMerge to increase throughput for two kernels that efficiently use device resources when run concurrently, and we establish that some kernels show worse performance when running concurrently. We also outline a method for using KernelMerge to investigate how concurrent kernels influence each other, with the goal of predicting runtimes for concurrent execution from individual kernel runtimes. Finally, we suggest GPU architectural changes that would improve such concurrent schedulers in the future.

Many multicore multiprocessors have a non-uniform memory architecture (NUMA), and for good performance, data and computations must be partitioned so that (ideally) all threads execute on the processor that holds their data. However, many multithreaded applications show heavy use of shared data structures that are accessed by all threads of the application. Automatic data placement and thread scheduling for these applications is (still) difficult.

We present a template library for shared data structures that allows a programmer to express both the data layout (how the data space is partitioned) as well as thread mapping and scheduling (when and where a thread is executed). The template library supports programmers in dividing computations and data for reducing the percentage of costly remote memory accesses in NUMA multicore multiprocessors. Initial experience with ferret, a program with irregular memory access patterns from the PARSEC benchmark suite, shows that this approach can reduce the number of remote accesses from 42% to 10% and results in a performance improvement of 3% without overwhelming the programmer.

Maze solving has been used as an example parallel programming problem for some years. Suggested solutions are often based on a sequential program, using work queues to allow multiple threads to explore different portions of the maze concurrently. This paper analyzes such an implementation, but also explores an alternative implementation based on strategies long used by human maze solvers. This alternative implementation outperforms the conventional approach on average, and furthermore exhibits large superlinear speedups. The paper uses insights into the cause of these superlinear speedups to derive a faster sequential algorithm, and finally considers further implications and future work.

Just as errors in sequential programs can lead to security exploits, errors in concurrent programs can lead to concurrency attacks. Questions such as whether these attacks are feasible and what characteristics they have remain largely unknown. In this paper, we present a preliminary study of concurrency attacks and the security implications of real world concurrency errors. Our study yields several interesting findings. For instance, we observe that the exploitability of a concurrency error depends on the duration of the timing window within which the error may occur. We further observe that attackers can increase this window through carefully crafted inputs. We also find that four out of five commonly used sequential defenses become unsafe when applied to concurrent programs. Based on our findings, we propose new defense directions and fixes to existing defenses.

In the multi-core era, it is critical to effectively test multi-threaded software and expose concurrency bugs before software release. Previous work has made a lot of progress in exercising the interleaving space and detecting concurrency bugs under a given input. Unfortunately, since software often has many test inputs and constant pressure to release new versions, existing techniques are still too expensive in practice. In this position paper, we use open-source software to study how interleavings, data races and atomicity violations particularly, overlap across test inputs and software versions. We also conduct preliminary explorations to improve the testing efficiency of multi-threaded software by avoiding redundant analysis across inputs and software versions.