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A significant percentage of the computing capacity of large-scale platforms is wasted due to interferences incurred by multiple applications that access a shared parallel file system concurrently. One solution to handling I/O bursts in large-scale HPC systems is to absorb them at an intermediate storage layer consisting of burst buffers. However, our analysis of the Argonne’s Mira system shows that burst buffers cannot prevent congestion at all times. As a consequence, I/O performance is dramatically degraded, showing in some cases a decrease in I/O throughput of 67%. In this paper, we analyze the effects of interference on application I/O bandwidth, and propose several scheduling techniques to mitigate congestion. We show through extensive experiments that our global I/O scheduler is able to reduce the effects of congestion, even on systems where burst buffers are used, and can increase the overall system throughput up to 56%. We also show that it outperforms current Mira I/O schedulers.

Florentino Sainz

DEEP Collective offload

Abstract:  We present a new extension of OmpSs programming model which allows users to dynamically offload C/C++ or Fortran code from one or many nodes to a group of remote nodes. Communication between remote nodes executing offloaded code is possible through MPI. It aims to improve programmability of Exascale and nowadays supercomputers which use different type of processors and interconnection networks which have to work together in order to obtain the best performance. We can find a good example of these architectures in the DEEP project, which has two separated clusters (CPUs and Xeon Phis).  With our technology, which works in any architecture which fully supports MPI, users will be able to easily offload work from the CPU cluster to the accelerators cluster without the constraint of falling back to the CPU cluster in order to perform MPI communications.