Engineering topics
Bunde, David P.
Publications and source records attributed to Bunde, David P..
Local search to improve coordinate-based task mapping
We present a local search strategy to improve the coordinate-based mapping of a parallel job’s tasks to the MPI ranks of its parallel allocation in order to reduce network congestion and the job’s communication time. The goal is to reduce the number of network hops between communicating pairs of ranks. Our target is applications with a nearest-neighbor stencil communication pattern running on mesh systems with non-contiguous processor allocation, such as Cray XE and XK Systems. Utilizing the miniGhost mini-app, which models the shock physics application CTH, we demonstrate that our strategy reduces application running time while also reducing the runtime variability. Furthermore, we further show that mapping quality can vary based on the selected allocation algorithm, even between allocation algorithms of similar apparent quality.
Using architecture information and real-time resource state to reduce power consumption and communication costs in parallel applications.
As computer systems grow in both size and complexity, the need for applications and run-time systems to adjust to their dynamic environment also grows. The goal of the RAAMP LDRD was to combine static architecture information and real-time system state with algorithms to conserve power, reduce communication costs, and avoid network contention. We devel- oped new data collection and aggregation tools to extract static hardware information (e.g., node/core hierarchy, network routing) as well as real-time performance data (e.g., CPU uti- lization, power consumption, memory bandwidth saturation, percentage of used bandwidth, number of network stalls). We created application interfaces that allowed this data to be used easily by algorithms. Finally, we demonstrated the benefit of integrating system and application information for two use cases. The first used real-time power consumption and memory bandwidth saturation data to throttle concurrency to save power without increasing application execution time. The second used static or real-time network traffic information to reduce or avoid network congestion by remapping MPI tasks to allocated processors. Results from our work are summarized in this report; more details are available in our publications [2, 6, 14, 16, 22, 29, 38, 44, 51, 54].
Combinatorial optimization for national security applications.
Abstract not provided.
Zoltan2: Exploiting Geometric Partitioning in Task Mapping for Parallel Computers.
Abstract not provided.
Task Mapping Stencil Computations for Non-Contiguous Allocations.
Abstract not provided.
Task Mapping Stencil Computations for Non-Contiguous Allocations.
Abstract not provided.
Task mapping stencil computations for non-contiguous allocations.
Abstract not provided.
Exploiting Geometric Partitioning in Task Mapping for Parallel Computers.
Abstract not provided.