Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of computing resources. The pressure to maintain reasonable levels of performance and portability forces application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical manycore architectures. In this paper, we study the benefits and limits of replacing the highly specialized internal scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them the opportunity to process and optimize its traversal in order to maximize the algorithm efficiency for the targeted hardware platform. A comparative study of the performance of the PaStiX solver on top of its native internal scheduler, PaRSEC, and StarPU frameworks, on different execution environments, is performed. The analysis highlights that these generic task-based runtimes achieve comparable results to the application-optimized embedded scheduler on homogeneous platforms. Furthermore, they are able to significantly speed up the solver on heterogeneous environments by taking advantage of the accelerators while hiding the complexity of their efficient manipulation from the programmer.Comment: Heterogeneity in Computing Workshop (2014

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

Task-based adaptive multiresolution for time-space multi-scale reaction-diffusion systems on multi-core architectures

A new solver featuring time-space adaptation and error control has been recently introduced to tackle the numerical solution of stiff reaction-diffusion systems. Based on operator splitting, finite volume adaptive multiresolution and high order time integrators with specific stability properties for each operator, this strategy yields high computational efficiency for large multidimensional computations on standard architectures such as powerful workstations. However, the data structure of the original implementation, based on trees of pointers, provides limited opportunities for efficiency enhancements, while posing serious challenges in terms of parallel programming and load balancing. The present contribution proposes a new implementation of the whole set of numerical methods including Radau5 and ROCK4, relying on a fully different data structure together with the use of a specific library, TBB, for shared-memory, task-based parallelism with work-stealing. The performance of our implementation is assessed in a series of test-cases of increasing difficulty in two and three dimensions on multi-core and many-core architectures, demonstrating high scalability

Towards an Adaptive Skeleton Framework for Performance Portability

The proliferation of widely available, but very different, parallel architectures makes the ability to deliver good parallel performance on a range of architectures, or performance portability, highly desirable. Irregularly-parallel problems, where the number and size of tasks is unpredictable, are particularly challenging and require dynamic coordination. The paper outlines a novel approach to delivering portable parallel performance for irregularly parallel programs. The approach combines declarative parallelism with JIT technology, dynamic scheduling, and dynamic transformation. We present the design of an adaptive skeleton library, with a task graph implementation, JIT trace costing, and adaptive transformations. We outline the architecture of the protoype adaptive skeleton execution framework in Pycket, describing tasks, serialisation, and the current scheduler.We report a preliminary evaluation of the prototype framework using 4 micro-benchmarks and a small case study on two NUMA servers (24 and 96 cores) and a small cluster (17 hosts, 272 cores). Key results include Pycket delivering good sequential performance e.g. almost as fast as C for some benchmarks; good absolute speedups on all architectures (up to 120 on 128 cores for sumEuler); and that the adaptive transformations do improve performance

Composable abstractions for synchronization in dynamic threading platforms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 259-269).High-level abstractions for parallel programming simplify the development of efficient parallel applications. In particular, composable abstractions allow programmers to construct a complex parallel application out of multiple components, where each component itself may be designed to exploit parallelism. This dissertation presents the design of three composable abstractions for synchronization in dynamic-threading platforms, based on ideas of task-graph execution, helper locks, and transactional memory. These designs demonstrate provably efficient runtime scheduling for programs with synchronization. For applications that use task-graph synchronization, I demonstrate provably efficient execution of task graphs with arbitrary dependencies as a library in a fork-join platform. Conventional wisdom suggests that a fork-join platform can execute an arbitrary task graph only with special runtime support or by converting the graph into a series-parallel computation which has less parallelism. By implementing Nabbit, a Cilk++ library for arbitrary task-graph execution, I show that one can in fact avoid introducing runtime modifications or additional constraints on parallelism. Nabbit achieves an asymptotically optimal completion-time bound for task graphs with constant degree. For applications that use lock-based synchronization, I introduce helper locks, a new synchronization abstraction that enables programmers to exploit asynchronous task parallelism inside locked critical regions. When a processor fails to acquire a helper lock, it can help complete the parallel critical region protected by the lock instead of simply waiting for the lock to be released. I also present HELPER, a runtime for supporting helper locks, and prove theoretical performance bounds which imply that HELPER achieves linear speedup on programs with a small number of highly parallel critical regions. For applications that use transaction-based synchronization, I present CWSTM, the first design of a transactional memory (TM) system that supports transactions with nested parallelism and nested parallel transactions of unbounded nesting depth. CWSTM demonstrates that one can provide theoretical bounds on the overhead of transaction conflict detection which are independent of nesting depth. I also introduce the concept of ownership-aware TM, the idea of using information about which memory locations a software module owns to provide provable guarantees of safety and correctness for open-nested transactions.by Jim Sukha.Ph.D