Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Exact parallel alignment of megabase genomic sequences with tunable work distribution

Sequence Alignment is a basic operation in Bioinformatics that is performed thousands of times, on daily basis. The exact methods for pairwise alignment have quadratic time complexity. For this reason, heuristic methods such as BLAST are widely used. To obtain exact results faster, parallel strategies have been proposed but most of them fail to align huge biological sequences. This happens because not only the quadratic time must be considered but also the space should be reduced. In this paper, we evaluate the performance of Z-align, a parallel exact strategy that runs in user-restricted memory space. Also, we propose and evaluate a tunable work distribution mechanism. The results obtained in two clusters show that two sequences of size 24MBP (Mega Base Pairs) and 23MBP, respectively, were successfully aligned with Z-align. Also, in order to align two 3MBP sequences, a speedup of 34.35 was achieved for 64 processors. The evaluation of our work distribution mechanism shows that the execution times can be sensibly reduced when appropriate parameters are chosen. Finally, when comparing Z-align with BLAST, it is clear that, in many cases, Z-align is able to produce alignments with higher score

Research on High-performance and Scalable Data Access in Parallel Big Data Computing

To facilitate big data processing, many dedicated data-intensive storage systems such as Google File System(GFS), Hadoop Distributed File System(HDFS) and Quantcast File System(QFS) have been developed. Currently, the Hadoop Distributed File System(HDFS) [20] is the state-of-art and most popular open-source distributed file system for big data processing. It is widely deployed as the bedrock for many big data processing systems/frameworks, such as the script-based pig system, MPI-based parallel programs, graph processing systems and scala/java-based Spark frameworks. These systems/applications employ parallel processes/executors to speed up data processing within scale-out clusters. Job or task schedulers in parallel big data applications such as mpiBLAST and ParaView can maximize the usage of computing resources such as memory and CPU by tracking resource consumption/availability for task assignment. However, since these schedulers do not take the distributed I/O resources and global data distribution into consideration, the data requests from parallel processes/executors in big data processing will unfortunately be served in an imbalanced fashion on the distributed storage servers. These imbalanced access patterns among storage nodes are caused because a). unlike conventional parallel file system using striping policies to evenly distribute data among storage nodes, data-intensive file systems such as HDFS store each data unit, referred to as chunk or block file, with several copies based on a relative random policy, which can result in an uneven data distribution among storage nodes; b). based on the data retrieval policy in HDFS, the more data a storage node contains, the higher the probability that the storage node could be selected to serve the data. Therefore, on the nodes serving multiple chunk files, the data requests from different processes/executors will compete for shared resources such as hard disk head and network bandwidth. Because of this, the makespan of the entire program could be significantly prolonged and the overall I/O performance will degrade. The first part of my dissertation seeks to address aspects of these problems by creating an I/O middleware system and designing matching-based algorithms to optimize data access in parallel big data processing. To address the problem of remote data movement, we develop an I/O middleware system, called SLAM, which allows MPI-based analysis and visualization programs to benefit from locality read, i.e, each MPI process can access its required data from a local or nearby storage node. This can greatly improve the execution performance by reducing the amount of data movement over network. Furthermore, to address the problem of imbalanced data access, we propose a method called Opass, which models the data read requests that are issued by parallel applications to cluster nodes as a graph data structure where edges weights encode the demands of load capacity. We then employ matching-based algorithms to map processes to data to achieve data access in a balanced fashion. The final part of my dissertation focuses on optimizing sub-dataset analyses in parallel big data processing. Our proposed methods can benefit different analysis applications with various computational requirements and the experiments on different cluster testbeds show their applicability and scalability

멀티 태스킹 환경에서 GPU를 사용한 범용적 계산 응용의 효율적인 시스템 자원 활용을 위한 GPU 시스템 최적화

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2020. 8. 염헌영.Recently, General Purpose GPU (GPGPU) applications are playing key roles in many different research fields, such as high-performance computing (HPC) and deep learning (DL). The common feature exists in these applications is that all of them require massive computation power, which follows the high parallelism characteristics of the graphics processing unit (GPU). However, because of the resource usage pattern of each GPGPU application varies, a single application cannot fully exploit the GPU systems resources to achieve the best performance of the GPU since the GPU system is designed to provide system-level fairness to all applications instead of optimizing for a specific type. GPU multitasking can address the issue by co-locating multiple kernels with diverse resource usage patterns to share the GPU resource in parallel. However, the current GPU mul- titasking scheme focuses just on co-launching the kernels rather than making them execute more efficiently. Besides, the current GPU multitasking scheme is not open-sourced, which makes it more difficult to be optimized, since the GPGPU applications and the GPU system are unaware of the feature of each other. In this dissertation, we claim that using the support from framework between the GPU system and the GPGPU applications without modifying the application can yield better performance. We design and implement the frame- work while addressing two issues in GPGPU applications. First, we introduce a GPU memory checkpointing approach between the host memory and the device memory to address the problem that GPU memory cannot be over-subscripted in a multitasking environment. Second, we present a fine-grained GPU kernel management scheme to avoid the GPU resource under-utilization problem in a i multitasking environment. We implement and evaluate our schemes on a real GPU system. The experimental results show that our proposed approaches can solve the problems related to GPGPU applications than the existing approaches while delivering better performance.최근 범용 GPU (GPGPU) 응용 프로그램은 고성능 컴퓨팅 (HPC) 및 딥 러닝 (DL)과 같은 다양한 연구 분야에서 핵심적인 역할을 수행하고 있다. 이러한 응 용 분야의 공통적인 특성은 거대한 계산 성능이 필요한 것이며 그래픽 처리 장치 (GPU)의 높은 병렬 처리 특성과 매우 적합하다. 그러나 GPU 시스템은 특정 유 형의 응용 프로그램에 최저화하는 대신 모든 응용 프로그램에 시스템 수준의 공정 성을 제공하도록 설계되어 있으며 각 GPGPU 응용 프로그램의 자원 사용 패턴이 다양하기 때문에 단일 응용 프로그램이 GPU 시스템의 리소스를 완전히 활용하여 GPU의 최고 성능을 달성 할 수는 없다. 따라서 GPU 멀티 태스킹은 다양한 리소스 사용 패턴을 가진 여러 응용 프로그 램을 함께 배치하여 GPU 리소스를 공유함으로써 GPU 자원 사용률 저하 문제를 해결할 수 있다. 그러나 기존 GPU 멀티 태스킹 기술은 자원 사용률 관점에서 응 용 프로그램의 효율적인 실행보다 공동으로 실행하는 데 중점을 둔다. 또한 현재 GPU 멀티 태스킹 기술은 오픈 소스가 아니므로 응용 프로그램과 GPU 시스템이 서로의 기능을 인식하지 못하기 때문에 최적화하기가 더 어려울 수도 있다. 본 논문에서는 응용 프로그램을 수정 없이 GPU 시스템과 GPGPU 응용 사 이의 프레임워크를 통해 사용하면 보다 높은 응용성능과 자원 사용을 보일 수 있음을 증명하고자 한다. 그러기 위해 GPU 태스크 관리 프레임워크를 개발하여 GPU 멀티 태스킹 환경에서 발생하는 두 가지 문제를 해결하였다. 첫째, 멀티 태 스킹 환경에서 GPU 메모리 초과 할당할 수 없는 문제를 해결하기 위해 호스트 메모리와 디바이스 메모리에 체크포인트 방식을 도입하였다. 둘째, 멀티 태스킹 환 경에서 GPU 자원 사용율 저하 문제를 해결하기 위해 더욱 세분화 된 GPU 커널 관리 시스템을 제시하였다. 본 논문에서는 제안한 방법들의 효과를 증명하기 위해 실제 GPU 시스템에 92 구현하고 그 성능을 평가하였다. 제안한 접근방식이 기존 접근 방식보다 GPGPU 응용 프로그램과 관련된 문제를 해결할 수 있으며 더 높은 성능을 제공할 수 있음을 확인할 수 있었다.Chapter 1 Introduction 1 1.1 Motivation 2 1.2 Contribution . 7 1.3 Outline 8 Chapter 2 Background 10 2.1 GraphicsProcessingUnit(GPU) and CUDA 10 2.2 CheckpointandRestart . 11 2.3 ResourceSharingModel. 11 2.4 CUDAContext 12 2.5 GPUThreadBlockScheduling . 13 2.6 Multi-ProcessServicewithHyper-Q 13 Chapter 3 Checkpoint based solution for GPU memory over- subscription problem 16 3.1 Motivation 16 3.2 RelatedWork. 18 3.3 DesignandImplementation . 20 3.3.1 System Design 21 3.3.2 CUDAAPIwrappingmodule 22 3.3.3 Scheduler . 28 3.4 Evaluation. 31 3.4.1 Evaluationsetup . 31 3.4.2 OverheadofFlexGPU 32 3.4.3 Performance with GPU Benchmark Suits 34 3.4.4 Performance with Real-world Workloads 36 3.4.5 Performance of workloads composed of multiple applications 39 3.5 Summary 42 Chapter 4 A Workload-aware Fine-grained Resource Manage- ment Framework for GPGPUs 43 4.1 Motivation 43 4.2 RelatedWork. 45 4.2.1 GPUresourcesharing 45 4.2.2 GPUscheduling . 46 4.3 DesignandImplementation . 47 4.3.1 SystemArchitecture . 47 4.3.2 CUDAAPIWrappingModule . 49 4.3.3 smCompactorRuntime . 50 4.3.4 ImplementationDetails . 57 4.4 Analysis on the relation between performance and workload usage pattern 60 4.4.1 WorkloadDefinition . 60 4.4.2 Analysisonperformancesaturation 60 4.4.3 Predict the necessary SMs and thread blocks for best performance . 64 4.5 Evaluation. 69 4.5.1 EvaluationMethodology. 70 4.5.2 OverheadofsmCompactor . 71 4.5.3 Performance with Different Thread Block Counts on Dif- ferentNumberofSMs 72 4.5.4 Performance with Concurrent Kernel and Resource Sharing 74 4.6 Summary . 79 Chapter 5 Conclusion. 81 요약. 92Docto

Design and Evaluation of a Collective IO Model for Loosely Coupled Petascale Programming

Loosely coupled programming is a powerful paradigm for rapidly creating higher-level applications from scientific programs on petascale systems, typically using scripting languages. This paradigm is a form of many-task computing (MTC) which focuses on the passing of data between programs as ordinary files rather than messages. While it has the significant benefits of decoupling producer and consumer and allowing existing application programs to be executed in parallel with no recoding, its typical implementation using shared file systems places a high performance burden on the overall system and on the user who will analyze and consume the downstream data. Previous efforts have achieved great speedups with loosely coupled programs, but have done so with careful manual tuning of all shared file system access. In this work, we evaluate a prototype collective IO model for file-based MTC. The model enables efficient and easy distribution of input data files to computing nodes and gathering of output results from them. It eliminates the need for such manual tuning and makes the programming of large-scale clusters using a loosely coupled model easier. Our approach, inspired by in-memory approaches to collective operations for parallel programming, builds on fast local file systems to provide high-speed local file caches for parallel scripts, uses a broadcast approach to handle distribution of common input data, and uses efficient scatter/gather and caching techniques for input and output. We describe the design of the prototype model, its implementation on the Blue Gene/P supercomputer, and present preliminary measurements of its performance on synthetic benchmarks and on a large-scale molecular dynamics application.Comment: IEEE Many-Task Computing on Grids and Supercomputers (MTAGS08) 200

Automated Genome-Wide Protein Domain Exploration

Exploiting the exponentially growing genomics and proteomics data requires high quality, automated analysis. Protein domain modeling is a key area of molecular biology as it unravels the mysteries of evolution, protein structures, and protein functions. A plethora of sequences exist in protein databases with incomplete domain knowledge. Hence this research explores automated bioinformatics tools for faster protein domain analysis. Automated tool chains described in this dissertation generate new protein domain models thus enabling more effective genome-wide protein domain analysis. To validate the new tool chains, the Shewanella oneidensis and Escherichia coli genomes were processed, resulting in a new peptide domain database, detection of poor domain models, and identification of likely new domains. The automated tool chains will require months or years to model a small genome when executing on a single workstation. Therefore the dissertation investigates approaches with grid computing and parallel processing to significantly accelerate these bioinformatics tool chains

Parallelisation for data-intensive applications over peer-to-peer networks

In Data Intensive Computing, properties of the data that are the input for an application decide running performance in most cases. Those properties include the size of the data, the relationships inside data, and so forth. There is a class of data intensive applications (BLAST, SETI@home, Folding@Home and so on so forth) whose performances solely depend on the amount of input data. Another important characteristic of those applications is that the input data can be split into units and these units are not related to each other during the runs of the applications. This characteristic helps this class of data intensive applications to be parallelised in the way where the input data is split into units and application runs on different computer nodes for certain portion of the units. SETI@home and Folding@Home have been successfully parallelised over peer-to-peer networks. However, they suffer from the problems of single point of failure and poor scalability. In order to solve these problems, we choose BLAST as our example data intensive applications and parallelise BLAST over a fully distributed peer-to-peer network. BLAST is a popular bioinformatics toolset which can be used to compare two DNA sequences. The major usage of BLAST is searching a query of sequences inside a database for their similarities so as to identify whether they are new. When comparing single pair of sequences, BLAST is efficient. However, due to growing size of the databases, executing BLAST jobs locally produces prohibitively poor performance. Thus, methods for parallelising BLAST are sought. Traditional BLAST parallelisation approaches are all based on clusters. Clusters employ a number of computing nodes and high bandwidth interlinks between nodes. Cluster-based BLAST exhibits higher performance; nevertheless, clusters suffer from limited resources and scalability problems. Clusters are expensive, prohibitively so when the growth of the sequence database are taken into account. It involves high cost and complication when increasing the number of nodes to adapt to the growth of BLAST databases. Hence a Peer-to-Peer-based BLAST service is required. This thesis demonstrates our parallelisation of BLAST over Peer-to-Peer networks (termed ppBLAST), which utilises the free storage and computing resources in the Peer-to-Peer networks to complete BLAST jobs in parallel. In order to achieve the goal, we build three layers in ppBLAST each of which is responsible for particular functions. The bottom layer is a DHT infrastructure with the support of range queries. It provides efficient range-based lookup service and storage for BLAST tasks. The middle layer is the BitTorrent-based database distribution. The upper layer is the core of ppBLAST which schedules and dispatches task to peers. For each layer, we conduct comprehensive research and the achievements are presented in this thesis. For the DHT layer, we design and implement our DAST-DHT. We analyse balancing, maximum number of children and the accuracy of the range query. We also compare the DAST with other range query methodology and state that if the number of children is adjusted to more two, the performance of DAST overcomes others. For the BitTorrent-like database distribution layer, we investigate the relationship between the seeding strategies and the selfish leechers (freeriders and exploiters). We conclude that OSS works better than TSS in a normal situation