Irregular and dynamic parallel applications pose significant challenges to achieving scalable performance on large-scale multicore clusters. These applications often require ongoing, dynamic load balancing in order to maintain efficiency. Scalable dynamic load balancing on large clusters is a challenging problem which can be addressed with distributed dynamic load balancing systems. Work stealing is a popular approach to distributed dynamic load balancing; however its performance on large-scale clusters is not well understood. Prior work on work stealing has largely focused on shared memory machines. In this work we investigate the design and scalability of work stealing on modern distributed memory systems. We demonstrate high efficiency and low overhead when scaling to 8,192 processors for three benchmark codes: a producer-consumer benchmark, the unbalanced tree search benchmark, and a multiresolution analysis kernel.

Abstract. This paper presents an unbalanced tree search (UTS) benchmark designed to evaluate the performance and ease of programming for parallel applications requiring dynamic load balancing. We describe algorithms for building a variety of unbalanced search trees to simulate different forms of load imbalance. We created versions of UTS in two parallel languages, OpenMP and Unified Parallel C (UPC), using work stealing as the mechanism for reducing load imbalance. We benchmarked the performance of UTS on various parallel architectures, including sharedmemory systems and PC clusters. We found it simple to implement UTS in both UPC and OpenMP, due to UPC's shared-memory abstractions. Results show that both UPC and OpenMP can support efficient dynamic load balancing on shared-memory architectures. However, UPC cannot alleviate the underlying communication costs of distributed-memory systems. Since dynamic load balancing requires intensive communication, performance portability remains difficult for applications such as UTS and performance degrades on PC clusters. By varying key work stealing parameters, we expose important tradeoffs between the granularity of load balance, the degree of parallelism, and communication costs.

We introduce Scioto, Shared Collections of Task Objects, a lightweight framework for providing task management on distributed memory machines under one-sided and globalview parallel programming models. Scioto provides locality aware dynamic load balancing and interoperates with MPI, ARMCI, and Global Arrays. Additionally, Scioto's task model and programming interface are compatible with many other existing parallel models including UPC, SHMEM, and CAF. Through task parallelism, the Scioto framework provides a solution for overcoming irregularity, load imbalance, and heterogeneity as well as dynamic mapping of computation onto emerging architectures. In this paper, we present the design and implementation of the Scioto framework and demonstrate its effectiveness on the Unbalanced Tree Search (UTS) benchmark and two quantum chemistry codes: the closed shell Self-Consistent Field (SCF) method and a sparse tensor contraction kernel extracted from a coupled cluster computation. We explore the efficiency and scalability of Scioto through these sample applications and demonstrate that is offers low overhead, achieves good performance on heterogeneous and multicore clusters, and scales to hundreds of processors.

This paper examines MPI's ability to support continuous, dynamic load balancing for unbalanced parallel applications. We use an unbalanced tree search benchmark (UTS) to compare two approaches, 1) work sharing using a centralized work queue, and 2) work stealing using explicit polling to handle steal requests. Experiments indicate that in addition to a parameter defining the granularity of load balancing, message-passing paradigms require additional parameters such as polling intervals to manage runtime overhead. Using these additional parameters, we observed an improvement of up to 2X in parallel performance. Overall we found that while work sharing may achieve better peak performance on certain workloads, work stealing achieves comparable if not better performance across a wider range of chunk sizes and workloads.

The Message Passing Interface (MPI) 3.0 standard, introduced in September 2012, includes a significant update to the one-sided communication interface, also known as remote memory access (RMA). In particular, the interface has been extended to better support popular one-sided and global-address-space parallel programming models to provide better access to hardware performance features and enable new data-access modes. We present the new RMA interface and specify formal axiomatic models for data consistency and access semantics. Such models can help users reason about details of the semantics that are hard to extract from the English prose in the standard. It also fosters the development of tools and compilers, enabling them to automatically analyze, optimize, and debug RMA programs.

Hybrid parallel programming with the message passing interface (MPI) for internode communication in conjunction with a shared-memory programming model to manage intranode parallelism has become a dominant approach to scalable parallel programming. While this model provides a great deal of flexibility and performance potential, it saddles programmers with the complexity of utilizing two parallel

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