A sampling-based approach for communication libraries auto-tuning

International audienceCommunication performance is a critical issue in HPC applications, and many solutions have been proposed on the literature (algorithmic, protocols, etc.) In the meantime, computing nodes become massively multicore, leading to a real imbalance between the number of communication sources and the number of physical communication resources. Thus it is now mandatory to share network boards between computation flows, and to take this sharing into account while performing communication optimizations. In previous papers, we have proposed a model and a framework for on-the-fly optimizations of multiplexed concurrent communication flows, and implemented this model in the \nm communication library. This library features optimization strategies able for example to aggregate several messages to reduce the number of packets emitted on the network, or to split messages to use several NICs at the same time. In this paper, we study the tuning of these dynamic optimization strategies. We show that some parameters and thresholds (\rdv threshold, aggregation packet size) depend on the actual hardware, both host and NICs. We propose and implement a method based on sampling of the actual hardware to auto-tune our strategies. Moreover, we show that multi-rail can greatly benefit from performance predictions. We propose an approach for multi-rail that dynamically balance the data between NICs using predictions based on sampling

Bulk Data Movement for Climate Dataset: Efficient Data Transfer Management with Dynamic Transfer Adjustment

Many scientific applications and experiments, such as high energy and nuclear physics, astrophysics, climate observation and modeling, combustion, nano-scale material sciences, and computational biology, generate extreme volumes of data with a large number of files. These data sources are distributed among national and international data repositories, and are shared by large numbers of geographically distributed scientists. A large portion of data is frequently accessed, and a large volume of data is moved from one place to another for analysis and storage. One challenging issue in such efforts is the limited network capacity for moving large datasets to explore and manage. The Bulk Data Mover (BDM), a data transfer management tool in the Earth System Grid (ESG) community, has been managing the massive dataset transfers efficiently with the pre-configured transfer properties in the environment where the network bandwidth is limited. Dynamic transfer adjustment was studied to enhance the BDM to handle significant end-to-end performance changes in the dynamic network environment as well as to control the data transfers for the desired transfer performance. We describe the results from the BDM transfer management for the climate datasets. We also describe the transfer estimation model and results from the dynamic transfer adjustment

Design and implementation of the fast send protocol

Over the last decades Internet traffic has grown dramatically. Besides the number of transfers, data sizes have risen as well. Traditional transfer protocols do not adapt to this evolution. Large-scale computational applications running on expensive parallel computers produce large amounts of data which often have to be transferred to weaker machines at the clients' premises. As parallel computers are frequently charged by the minute, it is indispensable to minimize the transfer time after computation succeeded to keep down costs. Consequently, the economic focus lies on minimizing the time to move away all data from the parallel computer whereas the actual time to arrival remains less (but still) important. This paper describes the design and implementation of a new transfer protocol, the Fast Send Protocol (FSP), which employs striping to intermediate nodes in order to minimize sending time and to utilize the sender's resources to a high extent

Potential Performance Bottleneck in Linux TCP

TCP is the most widely used transport protocol on the Internet today. Over the years, especially recently, due to requirements of high bandwidth transmission, various approaches have been proposed to improve TCP performance. The Linux 2.6 kernel is now preemptible. It can be interrupted mid-task, making the system more responsive and interactive. However, we have noticed that Linux kernel preemption can interact badly with the performance of the networking subsystem. In this paper we investigate the performance bottleneck in Linux TCP. We systematically describe the trip of a TCP packet from its ingress into a Linux network end system to its final delivery to the application; we study the performance bottleneck in Linux TCP through mathematical modeling and practical experiments; finally we propose and test one possible solution to resolve this performance bottleneck in Linux TCP

Failure-awareness and dynamic adaptation in data scheduling

Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific experiments in areas such as physics, biology, astronomy and earth sciences demand highly distributed resources to satisfy excessive computational requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application performance. Existing systems fail to address issues such as reliability, scalability, and efficiency in dealing with wide area data access, retrieval and processing. In this study, we explore data-intensive distributed computing and study challenges in data placement in distributed environments. After analyzing different application scenarios, we develop new data scheduling methodologies and the key attributes for reliability, adaptability and performance optimization of distributed data placement tasks. Inspired by techniques used in microprocessor and operating system architectures, we extend and adapt some of the known low-level data handling and optimization techniques to distributed computing. Two major contributions of this work include (i) a failure-aware data placement paradigm for increased fault-tolerance, and (ii) adaptive scheduling of data placement tasks for improved end-to-end performance. The failure-aware data placement includes early error detection, error classification, and use of this information in scheduling decisions for the prevention of and recovery from possible future errors. The adaptive scheduling approach includes dynamically tuning data transfer parameters over wide area networks for efficient utilization of available network capacity and optimized end-to-end data transfer performance