Mike Rainey

Scheduling parallel programs by work stealing with private deques

Work stealing has proven to be an effective method for scheduling parallel programs on multicore computers. To achieve high performance, work stealing distributes tasks between concurrent queues, called deques, which are assigned to each processor. Each processor operates on its deque locally except when performing load balancing via steals. Unfortunately, concurrent deques suffer from two limitations: 1) local deque operations require expensive memory fences in modern weak-memory architectures, 2) they can be very difficult to extend to support various optimizations and flexible forms of task distribution strategies needed many applications, e.g., those that do not fit nicely into the divide-and-conquer, nested data parallel paradigm. For these reasons, there has been a lot recent interest in implementations of work stealing with non-concurrent deques, where deques remain entirely private to each processor and load balancing is performed via message passing. Private deques eliminate the need for memory fences from local operations and enable the design and implementation of efficient techniques for reducing task-creation overheads and improving task distribution. These advantages, however, come at the cost of communication. It is not known whether work stealing with private deques enjoys the theoretical guarantees of concurrent deques and whether they can be effective in practice. In this paper, we propose two work-stealing algorithms with private deques and prove that the algorithms guarantee similar theoretical bounds as work stealing with concurrent deques. For the analysis, we use a probabilistic model and consider a new parameter, the branching depth of the computation. We present an implementation of the algorithm as a C++ library and show that it compares well to Cilk on a range of benchmarks. Since our approach relies on private deques, it enables implementing flexible task creation and distribution strategies. As a specific example, we show how to implement task coalescing and steal-half strategies, which can be important in fine-grain, non-divide-and-conquer algorithms such as graph algorithms, and apply them to the depth-first-search problem.

Manticore: a heterogeneous parallel language

The Manticore project is an effort to design and implement a new functional language for parallel programming. Unlike many earlier parallel languages, Manticore is a heterogeneous language that supports parallelism at multiple levels. Specifically, we combine CML-style explicit concurrency with NESL/Nepal-style data-parallelism. In this paper, we describe and motivate the design of the Manticore language. We also describe a flexible runtime model that supports multiple scheduling disciplines (e.g., for both fine-grain and course-grain parallelism) in a uniform framework. Work on a prototype implementation is ongoing and we give a status report.

Implicitly-threaded parallelism in Manticore

The increasing availability of commodity multicore processors is making parallel computing available to the masses. Traditional parallel languages are largely intended for large-scale scientific computing and tend not to be well-suited to programming the applications one typically finds on a desktop system. Thus we need new parallel-language designs that address a broader spectrum of applications. In this paper, we present Manticore, a language for building parallel applications on commodity multicore hardware including a diverse collection of parallel constructs for different granularities of work. We focus on the implicitly-threaded parallel constructs in our high-level functional language. We concentrate on those elements that distinguish our design from related ones, namely, a novel parallel binding form, a nondeterministic parallel case form, and exceptions in the presence of data parallelism. These features differentiate the present work from related work on functional data parallel language designs, which has focused largely on parallel problems with regular structure and the compiler transformations --- most notably, flattening --- that make such designs feasible. We describe our implementation strategies and present some detailed examples utilizing various mechanisms of our language.

A scheduling framework for general-purpose parallel languages

The trend in microprocessor design toward multicore and manycore processors means that future performance gains in software will largely come from harnessing parallelism. To realize such gains, we need languages and implementations that can enable parallelism at many different levels. For example, an application might use both explicit threads to implement course-grain parallelism for independent tasks and implicit threads for fine-grain data-parallel computation over a large array. An important aspect of this requirement is supporting a wide range of different scheduling mechanisms for parallel computation. In this paper, we describe the scheduling framework that we have designed and implemented for Manticore, a strict parallel functional language. We take a micro-kernel approach in our design: the compiler and runtime support a small collection of scheduling primitives upon which complex scheduling policies can be implemented. This framework is extremely flexible and can support a wide range of different scheduling policies. It also supports the nesting of schedulers, which is key to both supporting multiple scheduling policies in the same application and to hierarchies of speculative parallel computations. In addition to describing our framework, we also illustrate its expressiveness with several popular scheduling techniques. We present a (mostly) modular approach to extending our schedulers to support cancellation. This mechanism is essential for implementing eager and speculative parallelism. We finally evaluate our framework with a series of benchmarks and an analysis.

Status report: the manticore project

The Manticore project is an effort to design and implement a new functional language for parallel programming. Unlike many earlier parallel languages, Manticore is a heterogeneous language that supports parallelism at multiple levels. Specifically, we combine CML-style explicit concurrency with fine-grain, implicitly threaded, parallel constructs. We have been working on an implementation of Manticore for the past six months; this paper gives an overview of our design and a report on the status ofthe implementation effort.

Implicitly threaded parallelism in manticore

The increasing availability of commodity multicore processors is making parallel computing ever more widespread. In order to exploit its potential, programmers need languages that make the benefits of parallelism accessible and understandable. Previous parallel languages have traditionally been intended for large-scale scientific computing, and they tend not to be well suited to programming the applications one typically finds on a desktop system. Thus, we need new parallel-language designs that address a broader spectrum of applications. The Manticore project is our effort to address this need. At its core is Parallel ML, a high-level functional language for programming parallel applications on commodity multicore hardware. Parallel ML provides a diverse collection of parallel constructs for different granularities of work. In this paper, we focus on the implicitly threaded parallel constructs of the language, which support fine-grained parallelism. We concentrate on those elements that distinguish our design from related ones, namely, a novel parallel binding form, a nondeterministic parallel case form, and the treatment of exceptions in the presence of data parallelism. These features differentiate the present work from related work on functional data-parallel language designs, which have focused largely on parallel problems with regular structure and the compiler transformations—most notably, flattening—that make such designs feasible. We present detailed examples utilizing various mechanisms of the language and give a formal description of our implementation.

Oracle scheduling: controlling granularity in implicitly parallel languages

A classic problem in parallel computing is determining whether to execute a task in parallel or sequentially. If small tasks are executed in parallel, the task-creation overheads can be overwhelming. If large tasks are executed sequentially, processors may spin idle. This granularity problem, however well known, is not well understood: broadly applicable solutions remain elusive. We propose techniques for controlling granularity in implicitly parallel programming languages. Using a cost semantics for a general-purpose language in the style of the lambda calculus with support for parallelism, we show that task-creation overheads can indeed slow down parallel execution by a multiplicative factor. We then propose oracle scheduling, a technique for reducing these overheads, which bases granularity decisions on estimates of task-execution times. We prove that, for a class of computations, oracle scheduling can reduce task creation overheads to a small fraction of the work without adversely affecting available parallelism, thereby leading to efficient parallel executions. We realize oracle scheduling in practice by a combination of static and dynamic techniques. We require the programmer to provide the asymptotic complexity of every function and use run-time profiling to determine the implicit, architecture-specific constant factors. In our experiments, we were able to reduce overheads of parallelism down to between 3 and 13 percent, while achieving 6- to 10-fold speedups.

Data-only flattening for nested data parallelism

Data parallelism has proven to be an effective technique for high-level programming of a certain class of parallel applications, but it is not well suited to irregular parallel computations. Blelloch and others proposed nested data parallelism (NDP) as a language mechanism for programming irregular parallel applications in a declarative data-parallel style. The key to this approach is a compiler transformation that flattens the NDP computation and data structures into a form that can be executed efficiently on a wide-vector SIMD architecture. Unfortunately, this technique is ill suited to execution on todays multicore machines. We present a new technique, called data-only flattening, for the compilation of NDP, which is suitable for multicore architectures. Data-only flattening transforms nested data structures in order to expose programs to various optimizations while leaving control structures intact. We present a formal semantics of data-only flattening in a core language with a rewriting system. We demonstrate the effectiveness of this technique in the Parallel ML implementation and we report encouraging experimental results across various benchmark applications.

Lazy tree splitting

Nested data-parallelism (NDP) is a language mechanism that supports programming irregular parallel applications in a declarative style. In this paper, we describe the implementation of NDP in Parallel ML (PML), which is a part of the Manticore system. One of the main challenges of implementing NDP is managing the parallel decomposition of work. If we have too many small chunks of work, the overhead will be too high, but if we do not have enough chunks of work, processors will be idle. Recently, the technique of Lazy Binary Splitting was proposed to address this problem for nested parallel loops over flat arrays. We have adapted this technique to our implementation of NDP, which uses binary trees to represent parallel arrays. This new technique, which we call Lazy Tree Splitting (LTS), has the key advantage of performance robustness, i.e., it does not require tuning to get the best performance for each program. We describe the implementation of the standard NDP operations using LTS and present experimental data that demonstrate the scalability of LTS across a range of benchmarks.

A work-efficient algorithm for parallel unordered depth-first search

Advances in processing power and memory technology have made multicore computers an important platform for high-performance graph-search (or graph-traversal) algorithms. Since the introduction of multicore, much progress has been made to improve parallel breadth-first search. However, less attention has been given to algorithms for unordered or loosely ordered traversals. We present a parallel algorithm for unordered depth-first-search on graphs. We prove that the algorithm is work efficient in a realistic algorithmic model that accounts for important scheduling costs. This work-efficiency result applies to all graphs, including those with high diameter and high out-degree vertices. The algorithmic techniques behind this result include a new data structure for representing the frontier of vertices in depth-first search, a new amortization technique for controlling excess parallelism, and an adaptation of the lazy-splitting technique to depth first search. We validate the theoretical results with an implementation and experiments. The experiments show that the algorithm performs well on a range of graphs and that it can lead to significant improvements over comparable algorithms.