Resilient Optimistic Termination Detection for the Async-Finish Model

International audienceDriven by increasing core count and decreasing mean-time-to-failure in supercomputers, HPC runtime systems must improve support for dynamic task-parallel execution and resilience to failures. The async-finish task model, adapted for distributed systems as the asynchronous partitioned global address space programming model, provides a simple way to decompose a computation into nested task groups, each managed by a ‘finish’ that signals the termination of all tasks within the group.For distributed termination detection, maintaining a consistent view of task state across multiple unreliable processes requires additional book-keeping when creating or completing tasks and finish-scopes. Runtime systems which perform this book-keeping pessimistically, i.e. synchronously with task state changes, add a high communication overhead compared to non-resilient protocols. In this paper, we propose optimistic finish, the first message-optimal resilient termination detection protocol for the async-finish model. By avoiding the communication of certain task and finish events, this protocol allows uncertainty about the global structure of the computation which can be resolved correctly at failure time, thereby reducing the overhead for failure-free execution.Performance results using micro-benchmarks and the LULESH hydrodynamics proxy application show significant reductions in resilience overhead with optimistic finish compared to pessimistic finish. Our optimistic finish protocol is applicable to any task-based runtime system offering automatic termination detection for dynamic graphs of non-migratable tasks

The cooperative parallel: A discussion about run-time schedulers for nested parallelism

Nested parallelism is a well-known parallelization strategy to exploit irregular parallelism in HPC applications. This strategy also fits in critical real-time embedded systems, composed of a set of concurrent functionalities. In this case, nested parallelism can be used to further exploit the parallelism of each functionality. However, current run-time implementations of nested parallelism can produce inefficiencies and load imbalance. Moreover, in critical real-time embedded systems, it may lead to incorrect executions due to, for instance, a work non-conserving scheduler. In both cases, the reason is that the teams of OpenMP threads are a black-box for the scheduler, i.e., the scheduler that assigns OpenMP threads and tasks to the set of available computing resources is agnostic to the internal execution of each team. This paper proposes a new run-time scheduler that considers dynamic information of the OpenMP threads and tasks running within several concurrent teams, i.e., concurrent parallel regions. This information may include the existence of OpenMP threads waiting in a barrier and the priority of tasks ready to execute. By making the concurrent parallel regions to cooperate, the shared computing resources can be better controlled and a work conserving and priority driven scheduler can be guaranteed.Peer ReviewedPostprint (author's final draft

Parallel Tree Search in Volunteer Computing: a Case Study

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A high-performance matrix-matrix multiplication methodology for CPU and GPU architectures

Current compilers cannot generate code that can compete with hand-tuned code in efficiency, even for a simple kernel like matrix–matrix multiplication (MMM). A key step in program optimization is the estimation of optimal values for parameters such as tile sizes and number of levels of tiling. The scheduling parameter values selection is a very difficult and time-consuming task, since parameter values depend on each other; this is why they are found by using searching methods and empirical techniques. To overcome this problem, the scheduling sub-problems must be optimized together, as one problem and not separately. In this paper, an MMM methodology is presented where the optimum scheduling parameters are found by decreasing the search space theoretically, while the major scheduling sub-problems are addressed together as one problem and not separately according to the hardware architecture parameters and input size; for different hardware architecture parameters and/or input sizes, a different implementation is produced. This is achieved by fully exploiting the software characteristics (e.g., data reuse) and hardware architecture parameters (e.g., data caches sizes and associativities), giving high-quality solutions and a smaller search space. This methodology refers to a wide range of CPU and GPU architectures

Hoard: A scalable memory allocator for multithreaded applications

Parallel, multithreaded C and C++ programs such as web servers, database managers, news servers, and scientific applications are becoming increasingly prevalent. For these applications, the memory allocator is often a bottleneck that severely limits program performance and scalability on multiprocessor systems. Previous allocators suffer from problems that include poor performance and scalability, and heap organizations that introduce false sharing. Worse, many allocators exhibit a dramatic increase in memory consumption when confronted with a producer-consumer pattern of object allocation and freeing. This increase in memory consumption can range from a factor of P (the number of processors) to unbounded memory consumption

Distributed Computing for Enumeration

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