Pavan Balaji

Meeting Report: The Terabase Metagenomics Workshop and the Vision of an Earth Microbiome Project

Between July 18th and 24th 2010, 26 leading microbial ecology, computation, bioinformatics and statistics researchers came together in Snowbird, Utah (USA) to discuss the challenge of how to best characterize the microbial world using next-generation sequencing technologies. The meeting was entitled “Terabase Metagenomics” and was sponsored by the Institute for Computing in Science (ICiS) summer 2010 workshop program. The aim of the workshop was to explore the fundamental questions relating to microbial ecology that could be addressed using advances in sequencing potential. Technological advances in next-generation sequencing platforms such as the Illumina HiSeq 2000 can generate in excess of 250 billion base pairs of genetic information in 8 days. Thus, the generation of a trillion base pairs of genetic information is becoming a routine matter. The main outcome from this meeting was the birth of a concept and practical approach to exploring microbial life on earth, the Earth Microbiome Project (EMP). Here we briefly describe the highlights of this meeting and provide an overview of the EMP concept and how it can be applied to exploration of the microbiome of each ecosystem on this planet.

Addressing failures in exascale computing

We present here a report produced by a workshop on ‘Addressing failures in exascale computing’ held in Park City, Utah, 4–11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach. The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, and academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions.

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.

MPI on a Million Processors

Petascale machines with close to a million processors will soon be available. Although MPI is the dominant programming model today, some researchers and users wonder (and perhaps even doubt) whether MPI will scale to such large processor counts. In this paper, we examine this issue of how scalable is MPI. We first examine the MPI specification itself and discuss areas with scalability concerns and how they can be overcome. We then investigate issues that an MPI implementation must address to be scalable. We ran some experiments to measure MPI memory consumption at scale on up to 131,072 processes or 80% of the IBM Blue Gene/P system at Argonne National Laboratory. Based on the results, we tuned the MPI implementation to reduce its memory footprint. We also discuss issues in application algorithmic scalability to large process counts and features of MPI that enable the use of other techniques to overcome scalability limitations in applications.

A survey on resource allocation in high performance distributed computing systems

Classification of high performance computing (HPC) systems is provided.Current HPC paradigms and industrial application suites are discussed.State of the art in HPC resource allocation is reported.Hardware and software solutions are discussed for optimized HPC systems. An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

Survey of Techniques and Architectures for Designing Energy-Efficient Data Centers

Cloud computing has emerged as the leading paradigm for information technology businesses. Cloud computing provides a platform to manage and deliver computing services around the world over the Internet. Cloud services have helped businesses utilize computing services on demand with no upfront investments. The cloud computing paradigm has sustained its growth, which has led to increase in size and number of data centers. Data centers with thousands of computing devices are deployed as back end to provide cloud services. Computing devices are deployed redundantly in data centers to ensure 24/7 availability. However, many studies have pointed out that data centers consume large amount of electricity, thus calling for energy-efficiency measures. In this survey, we discuss research issues related to conflicting requirements of maximizing quality of services (QoSs) (availability, reliability, etc.) delivered by the cloud services while minimizing energy consumption of the data center resources. In this paper, we present the concept of inception of data center energy-efficiency controller that can consolidate data center resources with minimal effect on QoS requirements. We discuss software- and hardware-based techniques and architectures for data center resources such as server, memory, and network devices that can be manipulated by the data center controller to achieve energy efficiency.

Sockets Direct Protocol over InfiniBand in clusters: is it beneficial?

The Sockets Direct Protocol (SDP) had been proposed recently in order to enable sockets based applications to take advantage of the enhanced features provided by InfiniBand architecture. In this paper, we study the benefits and limitations of an implementation of SDP. We first analyze the performance of SDP based on a detailed suite of micro-benchmarks. Next, we evaluate it on two different real application domains: (1) A multitier data-center environment and (2) A Parallel Virtual File System (PVFS). Our micro-benchmark results show that SDP is able to provide up to 2.7 times better bandwidth as compared to the native sockets implementation over InfiniBand (IPoIB) and significantly better latency for large message sizes. Our experimental results also show that SDP is able to achieve a considerably higher performance (improvement of up to 2.4 times) as compared to IPoIB in the PVFS environment. In the data-center environment, SDP outperforms IPoIB for large file transfers inspite of currently being limited by a high connection setup time. However, this limitation is entirely implementation specific and as the InfiniBand software and hardware products are rapidly maturing, we expect this limitation to be overcome soon. Based on this, we have shown that the projected performance for SDP, without the connection setup time, can outperform IPoIB for small message transfers as well.

High performance user level sockets over Gigabit Ethernet

While a number of user-level protocols have been developed to reduce the gap between the performance capabilities of the physical network and the performance actually available, applications that have already been developed on kernel based protocols such as TCP have largely been ignored. There is a need to make these existing TCP applications take advantage of the modern user-level protocols such as EMP or VIA which feature both low-latency and high bandwidth. We have designed, implemented and evaluated a scheme to support such applications written using the sockets API to run over EMP without any changes to the application itself. Using this scheme, we are able to achieve a latency of 28.5 /spl mu/s for the Datagram sockets and 37 /spl mu/s for Data Streaming sockets compared to a latency of 120 /spl mu/s obtained by TCP for 4-byte messages. This scheme attains a peak bandwidth of around 840 Mbps. Both the latency and the throughput numbers are close to those achievable by EMP. The ftp application shows twice as much benefit on our sockets interface while the Web server application shows up to six times performance enhancement as compared to TCP. To the best of our knowledge, this is the first such design and implementation for Gigabit Ethernet.

Hybrid parallel programming with MPI and unified parallel C

The Message Passing Interface (MPI) is one of the most widely used programming models for parallel computing. However, the amount of memory available to an MPI process is limited by the amount of local memory within a compute node. Partitioned Global Address Space (PGAS) models such as Unified Parallel C (UPC) are growing in popularity because of their ability to provide a shared global address space that spans the memories of multiple compute nodes. However, taking advantage of UPC can require a large recoding effort for existing parallel applications. In this paper, we explore a new hybrid parallel programming model that combines MPI and UPC. This model allows MPI programmers incremental access to a greater amount of memory, enabling memory-constrained MPI codes to process larger data sets. In addition, the hybrid model offers UPC programmers an opportunity to create static UPC groups that are connected over MPI. As we demonstrate, the use of such groups can significantly improve the scalability of locality-constrained UPC codes. This paper presents a detailed description of the hybrid model and demonstrates its effectiveness in two applications: a random access benchmark and the Barnes-Hut cosmological simulation. Experimental results indicate that the hybrid model can greatly enhance performance; using hybrid UPC groups that span two cluster nodes, RA performance increases by a factor of 1.33 and using groups that span four cluster nodes, Barnes-Hut experiences a twofold speedup at the expense of a 2% increase in code size.

QoPS: A QoS Based Scheme for Parallel Job Scheduling

Although job scheduling has been much studied, the issue of providing deadline guarantees in this context has not been addressed. In this paper, we propose a new scheme, termed as QoPS to provide Quality of Service (QoS) in the response time given to the end user in the form of guarantees in the completion time to submitted independent parallel jobs. To the best of our knowledge, this scheme is the first one to implement admission control and guarantee deadlines for admitted parallel jobs.