High performance Java for multi-core systems

[Abstract] The interest in Java within the High Performance Computing (HPC) community has been rising during the last years thanks to its noticeable performance improvements and its productivity features. In a context where the trend to increase the number of cores per processor is leading to the generalization of many-core processors and accelerators, multithreading as an inherent feature of the language makes Java extremely interesting to exploit the performance provided by multi- and manycore architectures. This PhD Thesis presents a thorough analysis of the current state of the art regarding multi- and many-core programming in Java and provides the design, implementation and evaluation of several solutions to enable Java for the many-core era. To achieve this, a shared memory message-passing solution has been implemented to provide shared memory programming with the scalability of distributed memory paradigms, also with the benefits of a portable programming model that allows the developed codes to be run on distributed memory systems. Moreover, representative collective operations, involving computation and communication among different processes or threads, have been optimized, also introducing in Java new features for scalability from the MPI 3.0 specification, namely nonblocking collectives. Regarding the exploitation of many-core architectures, the lack of direct Java support forces to resort to wrappers or higher-level solutions to translate Java code into CUDA or OpenCL. The most relevant among these solutions have been evaluated and thoroughly analyzed in terms of performance and productivity. Guidelines for taking advantage of shared memory environments have been derived during the analysis and development of the proposed solutions, and the main conclusion is that the use of Java for shared memory programming on multi- and many-core systems is not only productive but also can provide high performance competitive results. However, in order to effectively take advantage of the underlying multi- and many-core architectures, the key is the availability of optimized middleware that abstracts multithreading details from the user, like the one proposed in this Thesis, and the optimization of common operations like collective communications

Design of efficient Java message-passing collectives on multi-core clusters

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-010-0464-5[Abstract] This paper presents a scalable and efficient Message-Passing in Java (MPJ) collective communication library for parallel computing on multi-core architectures. The continuous increase in the number of cores per processor underscores the need for scalable parallel solutions. Moreover, current system deployments are usually multi-core clusters, a hybrid shared/distributed memory architecture which increases the complexity of communication protocols. Here, Java represents an attractive choice for the development of communication middleware for these systems, as it provides built-in networking and multithreading support. As the gap between Java and compiled languages performance has been narrowing for the last years, Java is an emerging option for High Performance Computing (HPC). Our MPJ collective communication library increases Java HPC applications performance on multi-core clusters: (1) providing multi-core aware collective primitives; (2) implementing several algorithms (up to six) per collective operation, whereas publicly available MPJ libraries are usually restricted to one algorithm; (3) analyzing the efficiency of thread-based collective operations; (4) selecting at runtime the most efficient algorithm depending on the specific multi-core system architecture, and the number of cores and message length involved in the collective operation; (5) supporting the automatic performance tuning of the collectives depending on the system and communication parameters; and (6) allowing its integration in any MPJ implementation as it is based on MPJ point-to-point primitives. A performance evaluation on an InfiniBand and Gigabit Ethernet multi-core cluster has shown that the implemented collectives significantly outperform the original ones, as well as higher speedups when analyzing the impact of their use on collective communications intensive Java HPC applications. Finally, the presented library has been successfully integrated in MPJ Express (http://mpj-express.org), and will be distributed with the next release.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación; FPU; AP2009-2112Xunta de Galicia; PGIDIT06PXIB105228P

Parallel Pairwise Epistasis Detection on Heterogeneous Computing Architectures

This is a post-peer-review, pre-copyedit version of an article published in IEEE Transactions on Parallel and Distributed Systems. The final authenticated version is available online at: http://dx.doi.org/10.1109/TPDS.2015.2460247.[Abstract] Development of new methods to detect pairwise epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important task in bioinformatics as they can help to explain genetic influences on diseases. As these studies are time consuming operations, some tools exploit the characteristics of different hardware accelerators (such as GPUs and Xeon Phi coprocessors) to reduce the runtime. Nevertheless, all these approaches are not able to efficiently exploit the whole computational capacity of modern clusters that contain both GPUs and Xeon Phi coprocessors. In this paper we investigate approaches to map pairwise epistasic detection on heterogeneous clusters using both types of accelerators. The runtimes to analyze the well-known WTCCC dataset consisting of about 500 K SNPs and 5 K samples on one and two NVIDIA K20m are reduced by 27 percent thanks to the use of a hybrid approach with one additional single Xeon Phi coprocessor.Wellcome Trust; 076113Wellcome Trust; 085475Ministerio de Ecnomía y Competitividad; TIN2013-42148-

FastMPJ: a scalable and efficient Java message-passing library

This is a post-peer-review, pre-copyedit version of an article published in Cluster Computing. The final authenticated version is available online at: http://dx.doi.org/https://doi.org/10.1007/s10586-014-0345-4[Abstract] The performance and scalability of communications are key for high performance computing (HPC) applications in the current multi-core era. Despite the significant benefits (e.g., productivity, portability, multithreading) of Java for parallel programming, its poor communications support has hindered its adoption in the HPC community. This paper presents FastMPJ, an efficient message-passing in Java (MPJ) library, boosting Java for HPC by: (1) providing high-performance shared memory communications using Java threads; (2) taking full advantage of high-speed cluster networks (e.g., InfiniBand) to provide low-latency and high bandwidth communications; (3) including a scalable collective library with topology aware primitives, automatically selected at runtime; (4) avoiding Java data buffering overheads through zero-copy protocols; and (5) implementing the most widely extended MPI-like Java bindings for a highly productive development. The comprehensive performance evaluation on representative testbeds (InfiniBand, 10 Gigabit Ethernet, Myrinet, and shared memory systems) has shown that FastMPJ communication primitives rival native MPI implementations, significantly improving the efficiency and scalability of Java HPC parallel applications.Ministerio de Educación y Ciencia; AP2010-4348Ministerio de Economía y Competitividad; TIN2010-16735Xunta de Galicia; CN2012/211Xunta de Galicia; GRC2013/05

Performance Evaluation of Data-Intensive Computing Applications on a Public IaaS Cloud

[Abstract] The advent of cloud computing technologies, which dynamically provide on-demand access to computational resources over the Internet, is offering new possibilities to many scientists and researchers. Nowadays, Infrastructure as a Service (IaaS) cloud providers can offset the increasing processing requirements of data-intensive computing applications, becoming an emerging alternative to traditional servers and clusters. In this paper, a comprehensive study of the leading public IaaS cloud platform, Amazon EC2, has been conducted in order to assess its suitability for data-intensive computing. One of the key contributions of this work is the analysis of the storage-optimized family of EC2 instances. Furthermore, this study presents a detailed analysis of both performance and cost metrics. More specifically, multiple experiments have been carried out to analyze the full I/O software stack, ranging from the low-level storage devices and cluster file systems up to real-world applications using representative data-intensive parallel codes and MapReduce-based workloads. The analysis of the experimental results has shown that data-intensive applications can benefit from tailored EC2-based virtual clusters, enabling users to obtain the highest performance and cost-effectiveness in the cloud.Ministerio de Economía y Competitividad; TIN2013-42148-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/055Ministerio de Educación y Ciencia; AP2010-434

Performance analysis of HPC applications in the cloud

[Abstract] The scalability of High Performance Computing (HPC) applications depends heavily on the efficient support of network communications in virtualized environments. However, Infrastructure as a Service (IaaS) providers are more focused on deploying systems with higher computational power interconnected via high-speed networks rather than improving the scalability of the communication middleware. This paper analyzes the main performance bottlenecks in HPC application scalability on the Amazon EC2 Cluster Compute platform: (1) evaluating the communication performance on shared memory and a virtualized 10 Gigabit Ethernet network; (2) assessing the scalability of representative HPC codes, the NAS Parallel Benchmarks, using an important number of cores, up to 512; (3) analyzing the new cluster instances (CC2), both in terms of single instance performance, scalability and cost-efficiency of its use; (4) suggesting techniques for reducing the impact of the virtualization overhead in the scalability of communication-intensive HPC codes, such as the direct access of the Virtual Machine to the network and reducing the number of processes per instance; and (5) proposing the combination of message-passing with multithreading as the most scalable and cost-effective option for running HPC applications on the Amazon EC2 Cluster Compute platform.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Economía y Competitividad; AP2010-4348

Evaluation of messaging middleware for high-performance cloud computing

This is a post-peer-review, pre-copyedit version of an article published in Personal and Ubiquitous Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00779-012-0605-3[Abstract] Cloud computing is posing several challenges, such as security, fault tolerance, access interface singularity, and network constraints, both in terms of latency and bandwidth. In this scenario, the performance of communications depends both on the network fabric and its efficient support in virtualized environments, which ultimately determines the overall system performance. To solve the current network constraints in cloud services, their providers are deploying high-speed networks, such as 10 Gigabit Ethernet. This paper presents an evaluation of high-performance computing message-passing middleware on a cloud computing infrastructure, Amazon EC2 cluster compute instances, equipped with 10 Gigabit Ethernet. The analysis of the experimental results, confronted with a similar testbed, has shown the significant impact that virtualized environments still have on communication performance, which demands more efficient communication middleware support to get over the current cloud network limitations.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación y Ciencia; AP2010-434

Design of Scalable Java Communication Middleware for Multi-Core Systems

This is a post-peer-review, pre-copyedit version of an article published in The Computer Journal. The final authenticated version is available online at: https://doi.org/10.1093/comjnl/bxs122[Abstract] This paper presents smdev, a shared memory communication middleware for multi-core systems. smdev provides a simple and powerful messaging application program interface that is able to exploit the underlying multi-core architecture replacing inter-process and network-based communications by threads and shared memory transfers. The performance evaluation of smdev on several multi-core systems has shown noticeable improvements compared with other Java shared memory solutions, reaching and even overcoming the performance of natively compiled libraries. Thus, smdev has obtained start-up latencies around 0.76 μs and almost 90 Gbps bandwidth for point-to-point communications, as well as high performance and scalability both for collective operations and representative messaging kernels. This fact has motivated the integration of smdev in F-MPJ, our message-passing implementation in Java.Ministerio de Ciencia e Innovación; TIN2010-1673

General‐purpose computation on GPUs for high performance cloud computing

This is the peer reviewed version of the following article: Expósito, R. R., Taboada, G. L., Ramos, S., Touriño, J., & Doallo, R. (2013). General‐purpose computation on GPUs for high performance cloud computing. Concurrency and Computation: Practice and Experience, 25(12), 1628-1642., which has been published in final form at https://doi.org/10.1002/cpe.2845. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.[Abstract] Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynamically scalable resources as a service over the Internet. In addition, General‐Purpose computation on Graphical Processing Units (GPGPU) has gained much attention from scientific computing in multiple domains, thus becoming an important programming model in HPC. Compute Unified Device Architecture (CUDA) has been established as a popular programming model for GPGPUs, removing the need for using the graphics APIs for computing applications. Open Computing Language (OpenCL) is an emerging alternative not only for GPGPU but also for any parallel architecture. GPU clusters, usually programmed with a hybrid parallel paradigm mixing Message Passing Interface (MPI) with CUDA/OpenCL, are currently gaining high popularity. Therefore, cloud providers are deploying clusters with multiple GPUs per node and high‐speed network interconnects in order to make them a feasible option for HPC as a Service (HPCaaS). This paper evaluates GPGPU for high performance cloud computing on a public cloud computing infrastructure, Amazon EC2 Cluster GPU Instances (CGI), equipped with NVIDIA Tesla GPUs and a 10 Gigabit Ethernet network. The analysis of the results, obtained using up to 64 GPUs and 256‐processor cores, has shown that GPGPU is a viable option for high performance cloud computing despite the significant impact that virtualized environments still have on network overhead, which still hampers the adoption of GPGPU communication‐intensive applications. CopyrightMinisterio de Ciencia e Innovación; TIN2010-1673

Java in the High Performance Computing arena: Research, practice and experience

This is a post-peer-review, pre-copyedit version of an article published in Science of Computer Programming. The final authenticated version is available online at: https://doi.org/10.1016/j.scico.2011.06.002[Abstract] The rising interest in Java for High Performance Computing (HPC) is based on the appealing features of this language for programming multi-core cluster architectures, particularly the built-in networking and multithreading support, and the continuous increase in Java Virtual Machine (JVM) performance. However, its adoption in this area is being delayed by the lack of analysis of the existing programming options in Java for HPC and thorough and up-to-date evaluations of their performance, as well as the unawareness on current research projects in this field, whose solutions are needed in order to boost the embracement of Java in HPC. This paper analyzes the current state of Java for HPC, both for shared and distributed memory programming, presents related research projects, and finally, evaluates the performance of current Java HPC solutions and research developments on two shared memory environments and two InfiniBand multi-core clusters. The main conclusions are that: (1) the significant interest in Java for HPC has led to the development of numerous projects, although usually quite modest, which may have prevented a higher development of Java in this field; (2) Java can achieve almost similar performance to natively compiled languages, both for sequential and parallel applications, being an alternative for HPC programming; (3) the recent advances in the efficient support of Java communications on shared memory and low-latency networks are bridging the gap between Java and natively compiled applications in HPC. Thus, the good prospects of Java in this area are attracting the attention of both industry and academia, which can take significant advantage of Java adoption in HPC.Ministerio de Ciencia e Innovación; TIN2010-16735Ministerio de Educación, Cultura y Deporte; AP2009-211