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The recent addition of task parallelism to the OpenMP shared memory API allows programmers to express concurrency at a high level of abstraction and places the burden of scheduling parallel execution on the OpenMP run-time system. Efficient scheduling of tasks on modern multi-socket multicore shared memory systems requires careful consideration of an increasingly complex memory hierarchy, including shared caches and non-uniform memory access (NUMA) characteristics. In order to evaluate scheduling strategies, we extended the open source Qthreads threading library to implement different scheduler designs, accepting OpenMP programs through the ROSE compiler. Our comprehensive performance study of diverse OpenMP task-parallel benchmarks compares seven different task-parallel run-time scheduler implementations on an Intel Nehalem multi-socket multicore system: our proposed hierarchical work-stealing scheduler, a per-core work-stealing scheduler, a centralized scheduler, and LIFO and FIFO versions of the Qthreads round-robin scheduler. In addition, we compare our results against the Intel and GNU OpenMP implementations.Our hierarchical scheduling strategy leverages different scheduling methods at different levels of the hierarchy. By allowing one thread to steal work on behalf of all of the threads within a single chip that share a cache, the scheduler limits the number of costly remote steals. For cores on the same chip, a shared LIFO queue allows exploitation of cache locality between sibling tasks as well as between a parent task and its newly created child tasks. In the performance evaluation, our Qthreads hierarchical scheduler is competitive on all benchmarks tested. On five of the seven benchmarks, it demonstrates speedup and absolute performance superior to both the Intel and GNU OpenMP run-time systems. Our run-time also demonstrates similar performance benefits on AMD Magny Cours and SGI Altix systems, enabling several benchmarks to successfully scale to 192 CPUs of an SGI Altix.

The recent addition of task parallelism to the OpenMP shared memory API allows programmers to express concurrency at a high level of abstraction and places the burden of scheduling parallel execution on the OpenMP run time system. This is a welcome development for scientific computing as supercomputer nodes grow "fatter" with multicore and manycore processors. But efficient scheduling of tasks on modern multi-socket multicore shared memory systems requires careful consideration of an increasingly complex memory hierarchy, including shared caches and NUMA characteristics. In this paper, we propose a hierarchical scheduling strategy that leverages different methods at different levels of the hierarchy. By allowing one thread to steal work on behalf of all of the threads within a single chip that share a cache, our scheduler limits the number of costly remote steals. For cores on the same chip, a shared LIFO queue allows exploitation of cache locality between sibling tasks as well between a parent task and its newly created child tasks.We extended the open-source Qthreads threading library to implement our scheduler, accepting OpenMP programs through the ROSE compiler.We also present a comprehensive performance study of diverse OpenMP task parallel benchmarks, comparing seven different task parallel run time scheduler implementations on current generation multi-socket multicore systems: our hierarchical work stealing scheduler, a fully-distributed work stealing scheduler, a centralized scheduler, and LIFO and FIFO versions of the original Qthreads fullydistributed scheduler. In addition, we compare our results against OpenMP implementations from Intel and GCC. Hierarchical scheduling in Qthreads is competitive on all benchmarks. On several benchmarks, hierarchical scheduling in Qthreads demonstrates speedup and absolute performance superior to both the Intel and GCC OpenMP run time systems.

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This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to People who were influential in managing the project were: Bill Camp, Ed Barsis, Art Hale, and Neil Pundit While we have tried to be comprehensive in our listing of the people involved, it is very likely that we have missed at least one important contributor. The omission is a reflection of our poor memories and not a reflection of the importance of their contributions. We apologize to the unnamed contributors.4