Improving Data Movement Performance for Sparse Data Patterns on the Blue Gene/Q Supercomputer

Bui, Huy; Leigh, Jason; Jung, Eun-Sung; Vishwanathy, Venkatram; Papka, Michael E.

doi:10.1109/icppw.2014.47

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Some approaches focused on improving data locality and used a polynomial time file domain assignment algorithm to minimize communication during the shuffle phase [11]. Other researchers took the routing mechanism of BG/Q into consideration when issued sparse data access [12]. Tessier et al [13] used a different approach which combines an optimized buffering system and a topology-aware aggregators mapping algorithm targeting any kind of architecture, so the new algorithm can be easily extended.…”

Section: Related Workmentioning

confidence: 99%

Topology Aware Algorithm for Two-Phase I/O in Clusters With Tapered Hierarchical Networks

Liu

2020

IEEE Access

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It is important for large scale scientific simulations to read and write data from parallel file system efficiently. To decrease the I/O bottleneck of scientific simulations, many middle-ware solutions had been developed, and the two-phase scheme is one well-known I/O algorithm designed for collective I/O operations. During two-phase I/O based operations, a subset of processes is selected to aggregate noncontiguous pieces of data in the shuffle phase before doing collective reads/writes in the I/O phase. In the meantime, the tapered hierarchical network has long been proposed in order to decrease procurement and power cost. Higher bandwidth and lower latency can be provided in the low levels of tapered hierarchical network. In this paper, we presented a new implementation of two-phase I/O algorithm which takes into consideration the communication pattern and the topology of tapered hierarchical network when scheduling the inter-process communications during the shuffle phase. We validated the new algorithm on our high performance computers and obtained the experimental data on the I/O kernels of some simulations. A significant improvement of the shuffle phase performance was achieved by our new algorithm when compared with the previous two-phase I/O implementations.

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Section: Related Workmentioning

confidence: 99%

Topology Aware Algorithm for Two-Phase I/O in Clusters With Tapered Hierarchical Networks

Liu

2020

IEEE Access

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“…Certain approaches focus on discovering data locality and using a polynomial-time file domain to aggregator assignment algorithm to minimize communication between computing processes and aggregators [15]. Other researchers try to take the routing mechanism into consideration when issuing sparse data access on BG/Q [16]. Previous IBM supercomputers BG/P adopt a general method designed to increase the I/O bandwidth of collective I/O [17].…”

Section: Related Workmentioning

confidence: 99%

Topology-Aware Strategy for MPI-IO Operations in Clusters

Liu

Zhou

Guo

2018

Journal of Optimization

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This paper presents the topology-aware two-phase I/O (TATP), which optimizes the most popular collective MPI-IO implementation of ROMIO. In order to improve the hop-bytes metric during the file access, topology-aware two-phase I/O employs the Linear Assignment Problem (LAP) for finding an optimal assignment of file domain to aggregators, an aspect which is not considered in most two-phase I/O implementations. The distribution is based on the local data stored by each process, and its main purpose is to reduce the total hop-bytes of the I/O collective operation. Therefore, the global execution time can be improved. In most of the considered scenarios, topology-aware two-phase I/O obtains important improvements when compared with the original two-phase I/O implementations.

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“…Certain approaches focus on data locality and a polynomial time assignment algorithm (the Hungarian algorithm) to reduce the communication between compute nodes and aggregators [20]. Others have concentrated their efforts on the specific problem of sparse data patterns on the BG/Q, using an algorithm to take into account paths on the network topology [21]. A more general method designed to increase the I/O bandwidth of collective I/O for the previous version of IBM supercomputers BG/P has been proposed in [22].…”

Section: Related Workmentioning

confidence: 99%

TAPIOCA: An I/O Library for Optimized Topology-Aware Data Aggregation on Large-Scale Supercomputers

Tessier

Vishwanath

Jeannot

2017

2017 IEEE International Conference on Cluster Computing (CLUSTER)

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Reading and writing data efficiently from storage system is necessary for most scientific simulations to achieve good performance at scale. Many software solutions have been developed to decrease the I/O bottleneck. One well-known strategy, in the context of collective I/O operations, is the twophase I/O scheme. This strategy consists of selecting a subset of processes to aggregate contiguous pieces of data before performing reads/writes. In this paper, we present TAPIOCA, an MPI-based library implementing an efficient topology-aware twophase I/O algorithm. We show how TAPIOCA can take advantage of double-buffering and one-sided communication to reduce as much as possible the idle time during data aggregation. We also introduce our cost model leading to a topology-aware aggregator placement optimizing the movements of data. We validate our approach at large scale on two leadership-class supercomputers: Mira (IBM BG/Q) and Theta (Cray XC40). We present the results obtained with TAPIOCA on a micro-benchmark and the I/O kernel of a large-scale simulation. On both architectures, we show a substantial improvement of I/O performance compared with the default MPI I/O implementation. On BG/Q+GPFS, for instance, our algorithm leads to a performance improvement by a factor of twelve while on the Cray XC40 system associated with a Lustre filesystem, we achieve an improvement of four.

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Improving Data Movement Performance for Sparse Data Patterns on the Blue Gene/Q Supercomputer

Cited by 7 publications

References 19 publications

Topology Aware Algorithm for Two-Phase I/O in Clusters With Tapered Hierarchical Networks

Topology Aware Algorithm for Two-Phase I/O in Clusters With Tapered Hierarchical Networks

Topology-Aware Strategy for MPI-IO Operations in Clusters

TAPIOCA: An I/O Library for Optimized Topology-Aware Data Aggregation on Large-Scale Supercomputers

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