Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

OpenMP 5.0 added support for reductions over explicit tasks. This expands the previous reduction support that was limited primarily to worksharing and parallel constructs. While the scope of a reduction operation in a worksharing construct is the scope of the construct itself, the scope of a task reduction can vary. This difference requires syntactical means to define the scope of reductions, e.g., the task_reduction clause, and to associate participating tasks, e.g., the in_reduction clause. Furthermore, the disassociation of the number of threads and the number of tasks creates space for different implementations in the OpenMP runtime. In this work, we provide insights into the behavior and performance of task reduction implementations in GCC/g++ and LLVM/Clang. Our results indicate that task reductions are well supported by both compilers, but their performance differs in some cases and is often determined by the efficiency of the underlying task management.

OpenMP 5.0 added support for reductions over explicit tasks. This expands the previous reduction support that was limited primarily to worksharing and parallel constructs. While the scope of a reduction operation in a worksharing construct is the scope of the construct itself, the scope of a task reduction can vary. This difference requires syntactical means to define the scope of reductions, e.g., the task_reduction clause, and to associate participating tasks, e.g., the in_reduction clause. Furthermore, the disassociation of the number of threads and the number of tasks creates space for different implementations in the OpenMP runtime. In this work, we provide insights into the behavior and performance of task reduction implementations in GCC/g++ and LLVM/Clang. Our results indicate that task reductions are well supported by both compilers, but their performance differs in some cases and is often determined by the efficiency of the underlying task management.

Abstract not provided.

As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. Here, we describe the novel abstractions that have been added to Kokkos recently such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Multithreaded MPI applications are gaining popularity in scientific and high-performance computing. While the combination of programming models is suited to support current parallel hardware, it moves threading models and their interaction with MPI into focus. With the advent of new threading libraries, the flexibility to select threading implementations of choice is becoming an important usability feature. Open MPI has traditionally avoided componentizing its threading model, relying on code inlining and static initialization to minimize potential impacts on runtime fast paths and synchronization. This paper describes the implementation of a generic threading runtime support in Open MPI using the Opal Modular Component Architecture. This architecture allows the programmer to select a threading library at compile-or run-time, providing both static initialization of threading primitives as well as dynamic instantiation of threading objects. In this work, we present the implementation, define required interfaces, and discuss trade-offs of dynamic and static initialization.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.