A Parallel Algorithm for Compression of Big Next-Generation Sequencing Datasets

With the advent of high-throughput next-generation sequencing (NGS) techniques, the amount of data being generated represents challenges including storage, analysis and transport of huge datasets. One solution to storage and transmission of data is compression using specialized compression algorithms. However, these specialized algorithms suffer from poor scalability with increasing size of the datasets and best available solutions can take hours to compress gigabytes of data. In this paper we introduce paraDSRC, a parallel implementation of DSRC algorithm using a message passing model that presents reduction of the compression time complexity by a factor of O(1/p ). Our experimental results show that paraDSRC achieves compression times that are 43% to 99% faster than DSRC and compression throughputs of up to 8.4GB/s on a moderate size cluster. For many of the datasets used in our experiments super-linear speedups have been registered, making the implementation strongly scalable. We also show that paraDSRC is more than 25.6x faster than comparable parallel compression algorithms. The code will be available in author’s website if paper is accepted

Data Locality Aware Strategy for Two-Phase Collective I/O

Abstract. This paper presents Locality-Aware Two-Phase (LATP) I/O, an opti-mization of the Two-Phase collective I/O technique from ROMIO, the most pop-ular MPI-IO implementation. In order to increase the locality of the file accesses, LATP employs the Linear Assignment Problem (LAP) for finding an optimal dis-tribution of data to processes, an aspect that is not considered in the original tech-nique. This assignment is based on the local data that each process stores and has as main purpose the reduction of the number of communication involved in the I/O collective operation and, therefore, the improvement of the global execution time. Compared with Two-Phase I/O, LATP I/O obtains important improvements in most of the considered scenarios.

Data Redistribution using One-sided Transfers to In-memory HDF5 Files

International audienceOutputs of simulation codes making use of the HDF5 file format are usually and mainly composed of several different attributes and datasets, storing either lightweight pieces of information or containing heavy parts of data. These objects, when written or read through the HDF5 layer, create metadata and data IO operations of different block sizes, which depend on the precision and dimension of the arrays that are being manipulated. By making use of simple block redistribution strategies, we present in this paper a case study showing HDF5 IO performance improvements for "in-memory" files stored in a distributed shared memory buffer using one-sided communications through the HDF5 API

H5hut: A high-performance I/O library for particle-based simulations

Particle-based simulations running on large high-performance computing systems over many time steps can generate an enormous amount of particle- and field-based data for post-processing and analysis. Achieving high-performance I/O for this data, effectively managing it on disk, and interfacing it with analysis and visualization tools can be challenging, especially for domain scientists who do not have I/O and data management expertise. We present the H5hut library, an implementation of several data models for particle-based simulations that encapsulates the complexity of HDF5 and is simple to use, yet does not compromise performance

Improving the performance of the Parallel Ice Sheet Model on a large-scale, distributed supercomputer

In this thesis, we describe our work to understand and improve the performance and scalability of the Parallel Ice Sheet Model (PISM) on the Ranger supercomputer. PISM enables the simulation of large-scale ice sheets, such as those found in Greenland and Antarctica, which are of particular interest to climate scientists due to their potential to contribute to sea-level rise. PISM has a unique parallel architecture that is designed to take advantage of the computational resources available on state-of-the-art supercomputers. The problem, however, is that even though PISM can run without modifcation on a supercomputer, it is generally unable to do so efficiently. We observed that PISM exhibits rapidly diminishing performance gains as the number of processors is increased, even experiencing an increase in execution time with large processor counts. PISM\u27s inability to make efficient use of the resources available on today\u27s supercomputers presents a challenge to researchers, particularly as larger and higher resolution data sets become available. In this work, we analyzed the reasons for PISM\u27s poor performance and developed techniques to address these issues, resulting in an increase in performance by as much as a factor of 20

Improving Parallel I/O Performance Using Interval I/O

Today\u27s most advanced scientific applications run on large clusters consisting of hundreds of thousands of processing cores, access state of the art parallel file systems that allow files to be distributed across hundreds of storage targets, and utilize advanced interconnections systems that allow for theoretical I/O bandwidth of hundreds of gigabytes per second. Despite these advanced technologies, these applications often fail to obtain a reasonable proportion of available I/O bandwidth. The reasons for the poor performance of application I/O include the noncontiguous I/O access patterns used for scientific computing, contention due to false sharing, and the somewhat finicky nature of parallel file system performance. We argue that a more fundamental cause of this problem is the legacy view of a file as a linear sequence of bytes. To address these issues, we introduce a novel approach for parallel I/O called Interval I/O. Interval I/O is an innovative approach that uses application access patterns to partition a file into a series of intervals, which are used as the fundamental unit for subsequent I/O operations. Use of this approach provides superior performance for the noncontiguous access patterns which are frequently used by scientific applications. In addition, the approach reduces false contention and the unnecessary serialization it causes. Interval I/O also significantly increases the performance of atomic mode operations. Finally, the Interval I/O approach includes a technique for supporting parallel I/O for cooperating applications. We provide a prototype implementation of our Interval I/O system and use it to demonstrate performance improvements of as much as 1000% compared to ROMIO when using Interval I/O with several common benchmarks

Optimizing Noncontiguous Accesses in MPI-IO

The I/O access patterns of many parallel applications consist of accesses to a large number of small, noncontiguous pieces of data. If an application’s I/O needs are met by making many small, distinct I/O requests, however, the I/O performance degrades drastically. To avoid this problem, MPI-IO allows users to access noncontiguous data with a single I/O function call, unlike in Unix I/O. In this paper, we explain how critical this feature of MPI-IO is for high performance and how it enables implementations to perform optimizations. We first provide a classification of the different ways of expressing an application’s I/O needs in MPI-IO—we classify them into four levels, called level 0 through level 3. We demonstrate that, for applications with noncontiguous access patterns, the I/O performance improves dramatically if users write their applications to make level-3 requests (noncontiguous, collective) rather than level-0 requests (Unix style). We then describe how our MPI-IO implementation, ROMIO, delivers high performance for noncontiguous requests. We explain in detail the two key optimizations ROMIO performs: data sieving for noncontiguous requests from one process and collective I/O for noncontiguous requests from multiple processes. We describe how we have implemented these optimizations portably on multiple machines and file systems, controlled their memory requirements, and also achieved high performance. We demonstrate the performance and portabilit

A Study of Client-based Caching for Parallel I/O

The trend in parallel computing toward large-scale cluster computers running thousands of cooperating processes per application has led to an I/O bottleneck that has only gotten more severe as the the number of processing cores per CPU has increased. Current parallel file systems are able to provide high bandwidth file access for large contiguous file region accesses; however, applications repeatedly accessing small file regions on unaligned file region boundaries continue to experience poor I/O throughput due to the high overhead associated with accessing parallel file system data. In this dissertation we demonstrate how client-side file data caching can improve parallel file system throughput for applications performing frequent small and unaligned file I/O. We explore the impacts of cache page size and cache capacity using the popular FLASH I/O benchmark and explore a novel cache sharing approach that leverages the trend toward multi-core processors. We also explore a technique we call progressive page caching that represents cache data using dynamic data structures rather than fixed-size pages of file data. Finally, we explore a cache aggregation scheme that leverages the high-level file I/O interfaces provided by the PVFS file system to provide further performance enhancements. In summary, our results indicate that a correctly configured middleware-based file data cache can dramatically improve the performance of I/O workloads dominated by small unaligned file accesses. Further, we demonstrate that a well designed cache can offer stable performance even when the selected cache page granularity is not well matched to the provided workload. Finally, we have shown that high-level file system interfaces can significantly accelerate application performance, and interfaces beyond those currently envisioned by the MPI-IO standard could provide further performance benefits