Performance Evaluation of Collective Write Algorithms in MPI I/O

Chaarawi, Mohamad; Chandok, Suneet; Gabriel, Edgar

doi:10.1007/978-3-642-01970-8_19

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…While I/O tuning is probably the first step to increase I/O bandwidth on new architectures [4], [5], [6], improvements at different layers of the I/O software stack are also necessary. From a file system perspective, GPFS [7] [9] evaluate various collective I/O write algorithms. Other approaches to optimizing collective I/O have also been undertaken using techniques such as process placement based on the I/O pattern [10] or collective I/O autotuning with machine learning [11].…”

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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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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“…In these, collective I/O allows to achieve improved performance. For this, Chaarawi et al [4] evaluate various collective I/O write algorithms. Other approaches to optimizing collective I/O have also been undertaken using techniques such as process placement based on the I/O pattern [19] or collective I/O autotuning with machine learning [11].…”

Section: Related Workmentioning

confidence: 99%

Topology-Aware Data Aggregation for Intensive I/O on Large-Scale Supercomputers

Tessier

Malakar²,

Vishwanath³

et al. 2016

2016 First International Workshop on Communication Optimizations in HPC (COMHPC)

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Reading and writing data efficiently from storage systems is critical for high performance data-centric applications. These I/O systems are being increasingly characterized by complex topologies and deeper memory hierarchies. Effective parallel I/O solutions are needed to scale applications on current and future supercomputers. Data aggregation is an efficient approach consisting of electing some processes in charge of aggregating data from a set of neighbors and writing the aggregated data into storage. Thus, the bandwidth use can be optimized while the contention is reduced. In this work, we take into account the network topology for mapping aggregators and we propose an optimized buffering system in order to reduce the aggregation cost. We validate our approach using micro-benchmarks and the I/O kernel of a large-scale cosmology simulation. We show improvements up to 15× faster for I/O operations compared to a standard implementation of MPI I/O.

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“…Some variants and optimizations have been proposed in order to improve the performance of two-phase methods. Chaarawi et al [15] presented segmentation algorithms derived from ROMIO's collective I/O method, and Sehrish et al [16] proposed a multibuffer pipelining optimization that overlaps the request aggregation phase and the I/O phase.…”

Section: Related Workmentioning

confidence: 99%

Implementation and Evaluation of MPI Nonblocking Collective I/O

Seo

Latham

Zhang

et al. 2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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The well-known gap between relative CPU speeds and storage bandwidth results in the need for new strategies for managing I/O demands. In large-scale MPI applications, collective I/O has long been an effective way to achieve higher I/O rates, but it poses two constraints. First, although overlapping collective I/O and computation represents the next logical step toward a faster time to solution, MPI's existing collective I/O API provides only limited support for doing so. Second, collective routines (both for I/O and communication) impose a synchronization cost in addition to a communication cost. The upcoming MPI 3.1 standard will provide a new set of nonblocking collective I/O operations to satisfy the need of applications. We present here initial work on the implementation of MPI nonblocking collective I/O operations in the MPICH MPI library. Our implementation begins with the extended two-phase algorithm used in ROMIO's collective I/O implementation. We then utilize a state machine and the extended generalized request interface to maintain the progress of nonblocking collective I/O operations. The evaluation results indicate that our implementation performs as well as blocking collective I/O in terms of I/O bandwidth and is capable of overlapping I/O and other operations. We believe that our implementation can help users try nonblocking collective I/O operations in their applications.

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Performance Evaluation of Collective Write Algorithms in MPI I/O

Cited by 12 publications

References 7 publications

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

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

Topology-Aware Data Aggregation for Intensive I/O on Large-Scale Supercomputers

Implementation and Evaluation of MPI Nonblocking Collective I/O

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