High performance horizons [high performance computing

The US Department of Energy (DOE) Exascale Computing Project (ECP) has fostered and strengthened the use of modern software engineering practices for developing applications and libraries, and this effort has resulted in the coordinated and interoperable E4S1 and xSDK2 ecosystems. Although this approach is cost-effective, it relies on robust programming systems and tools (PST) as the underlying foundation for our HPC software. At present, our primary PST stack consists of traditional high-performance computing (HPC) languages, namely Fortran, C, C++, and the popular Python language for data analysis and AI workflows. These languages support various programming frameworks and run-time abstractions that enable parallelism and concurrency across multiple node architectures and thousands of nodes through a variety of interconnect systems. However, to accommodate users’ diverse needs, certain aspects of the HPC ecosystem are delegated to vendor-specific or third-party implementations that extend beyond a particular scientific domain. This broader scope results in a multitude of specifications and variations, which leads to a complex orchestration of many-ecosystems. Unfortunately, this complexity in the ecosystem imposes additional overhead costs on consumers during the latter stages of the development cycle. In addition to the software ecosystem challenge, the upcoming conclusion of the ECP by December 2023 has raised significant concerns within the HPC programming systems community, from both the economic and social perspectives. The ECP has implemented a management structure for software development and funding decisions across all ECP participants by following a conventional hierarchical and centralized approach. However, this structure has prompted certain considerations within the community, particularly in anticipation of the Software Sustainability initiative by the DOE’s Advanced Scientific Computing Research Program (ASCR). For the success of this new initiative, it is of utmost importance to secure consistent funding and foster close engagement with researchers and core developers of existing programming-system products. This collaboration is vital to maintaining the critical capabilities of the current software during the transition phase while proactively adapting to future technology and workforce trends. The community recognizes the significance of adapting to emerging trends and is aware of the inherent fragility of the HPC software ecosystem, particularly in relation to programming systems that cater to all users. The ability to adapt and evolve is essential to staying relevant and effectively addressing these technical, economic, and social challenges. The S4PST team, which represents one of the six ASCR Software Sustainability seedling projects, is dedicated to tackling these challenges through community-based approaches that go beyond the scope of the DOE. This involves collaboration between national laboratories with academia, non-DOE institutions, hardware and system vendors, and international partners. By fostering these partnerships, we aim to create a robust and sustainable HPC software ecosystem that can effectively meet the needs of the community. This new community effort, driven by the eight DOE labs, will take on the responsibility of guiding funding decisions for programming-systems development and maintenance with transparency and consistency across all decisions. Additionally, the team will offer common technical services to the programming systems community, irrespective of their funding situations, and facilitate community-wide incubation to proactively nurture the software ecosystem. By actively engaging with stakeholders and employing a collaborative approach, we can collectively shape the future of programming systems and ensure a robust and thriving HPC software landscape. On May 11–12, 2023, the S4PST team conducted its inaugural kick-off workshop at the Innovative Computing Laboratory (ICL) in the University of Tennessee, Knoxville, hosted by Hartwig Anzt. The workshop encompassed various sessions dedicated to presentations and discussions, with the aim of comprehending the team members’ perspectives on the vision of software sustainability. Additionally, the workshop aimed to identify the technical, economic, and social requirements for sustaining the programming-systems community in the field of HPC. This report provides a summary of the S4PST effort by highlighting five major thrust areas discussed during the workshop: (i) community, (ii) technical support, (iii) training and diversity, (iv) verification, validation and correctness, and (v) emerging technologies. It also encompasses an overview of the presentations and discussions held throughout the event, our views and potential synergies with other seedling efforts, along with the outcomes and key takeaways from our initial discussions.

The research area of Multimedia Content Analysis (MMCA) considers all aspects of the automated extraction of knowledge from multimedia archives and data streams. To satisfy the increasing computational demands of emerging MMCA problems, there is an urgent need to apply High Performance Computing (HPC) techniques. As most MMCA researchers are not also experts in the field of HPC, there is a demand for programming models and tools that can help MMCA researchers in applying these techniques. Ideally, such models and tools should be efficient and easy to use.At present there are several user transparent library-based tools available that aim to satisfy both these conditions. All such tools use a data parallel approach in which data structures (e.g. video frames) are scattered among the available compute nodes. However, for certain MMCA applications a data parallel approach induces intensive communication, which significantly decreases performance. In these situations, we can benefit from applying alternative parallelization approaches.This paper presents an innovative user transparent programming model for MMCA applications that employs task parallelism. We show our programming model to be a viable alternative that is capable of outperforming existing user transparent data parallel approaches. As a result, the model is an important next step towards our goal of integrating data and task parallelism under a familiar sequential programming interface.KeywordsSource NodeApplication Programming InterfaceHigh Performance ComputingTask GraphRuntime SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper presents the design and implementation of an energy saving daemon for clusters called cherub. The design of the cherub daemon is modular and extensible. In the field of High Performance Computing (HPC) well known Resource Management Systems (RMSs) like Portable Batch System (PBS) [8]or its open source derivative, the TORQUE resource manager [11]are used to manage clusters and work queues. Thus, cherub is able to interact with different RMSs to make them energy aware. cherub is also suited for load-balancing clusters managed by dispatchers like Linux Virtual Server (LVS) [17] since the only requirement is a central approach for resource management.

Nonlinear numerical control is rightfully considered to be one of the most difficult engineering problems to tackle, in terms of both practical implementation and time-to-solution. It requires time-stepping numerical models for simulating the trajectory of the system, adjoint models for sensitivity analysis and matrix-free iterative solvers to produce the solution field of the inverse problem. The field of high-performance computing (HPC) provides computational tools and practices which enable the deployment of numerical applications on computational clusters, supercomputers and cloud computing facilities. This article presents a set of practical methods aimed at accelerating and parallelizing computation for sensitivity analysis in a large-scale 4D-Var data assimilation setting.

In the scientific domain, extremely large amounts of data are generated by large-scale high performance computing (HPC) simulations. Storing and sending such vast volumes of data poses serious scalability and performance issues, which can be considerably mitigated by data compression techniques which significantly reduced storage size and data movement burdens. Since scientific data are being shared by scientists more and more frequently, data security methods that ensure the confidentiality, integrity, and availability of data itself are becoming increasingly important. As such, combing compression and encryption is critical to storing large-scale datasets securely. In this work, we explore how to integrate data compression and cryptography techniques as efficiently as possible for big scientific datasets in the HPC field. We perform thorough experiments using different scientific datasets with the state-of-the-art error-bounded lossy compressor - SZ - on a real-world supercomputing environment. Experiments verify that performing encryption before lossy compression (a.k.a., encr-cmpr method) may invalidate the advantage of compression algorithms. By contrast, executing encryption after lossy compression (a.k.a., cmpr-encr method) keeps not only high compression ratios but high overall execution speed. Experiments also verify that the encryption overhead under the cmpr-encr method decreases with increasing compression ratios, which means very good scalability.

This work focuses on possibilities of practical using public cloud in comparison with high-performance cluster for difficult calculations. A task dealing with problem of coloring the edges of a large set of cubic graphs and examining relations between these graphs was used as a criterion. In general, we dealt with couple of modifications and levels of difficulty of a given task. Assessing the convenience of usage nowadays, not only computer resources, is significant factor and phenomenon an environmental friendliness. In the world of informational technology and in the field of high-performance computing, is established the idea of green computing. Algorithms for effective usage of available computing resources are used to reach the aim within the high-performance computing that is hidden under the green computing. In the connection with service providers of public cloud computing is solving of securing datacenter power from renewable sources an added factor.

An architecture of high performance computer,called Hyper Parallel Processing(HPP),is proposed to satisfy the requirements of both High Performance Computing,and Utility Computing which will be the application model of data centers.HPP combines the benefits of the scalability of MPP,the communication efficiency of DSM,as well as the commodity of cluster.Comparisons of current main-stream high performance computer architectures show that none of them can satisfy both HPC and utility computing.The main features of HPP are Global Address Space(GAS) and Hyper Node with single Operating System image.HPP supports the distributed global address space including both memory and I/O,but without hardware cache coherence.A Hyper Node consists of a set of application specific CPUs and a(or more) OS specific CPU.The OS CPU maintains the single system image,while the application CPUs run only lightweight run-time software.Besides the GAS interconnect network for applications,a standard SAN connects all OS CPUs and I/O devices providing system management and storage service.HPP is able to provide many opportunities of innovative research in High Performance and Utility Computing areas,including communication,synchronization,programming model,node operating system,utility computing,fault isolation,CPU and system etc.According to HPP architecture,a prototype system of Dawning5000 HPC is implemented and the feasibility of HPP is proved.

The increased computational performance in science and engineering has led to the strong need for arithmetic intensive parallel computing, CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) are parallel computing technologies proposed in recent years, which both have a great expanse of application prospect in the field of high performance computing. In this paper, we discuss the parallel computing technologies based on CUDA and OpenCL by applying them on the smoking simulation based on real-time physics. Real-time physics requires high real-time quality and authenticity. We build the 3D smoking simulation model using the Computational Fluid Dynamics method and render the smoke using 3D texture volume rendering method to represent the 3D smoking simulation veridical and accurately. In order to ensure the real-time quality of 3D smoking simulation, we respectively adopt CUDA and OpenCL parallel computing technologies applied on different hardware platforms, achieving the solving of 3D smoking simulation in parallel. Experimental results show that compared with CPU serial programming, CUDA and OpenCL parallel computing technologies have predominant operability and inevitable efficiency advantages on arithmetic intensive parallel computing.

For decades, HPC has established itself as an essential tool for discoveries, innovations and new insights in science, research and development, engineering and business across a wide range of application areas in academia and industry. Today High-Performance Computing is also well recognized to be of strategic and economic value – HPC matters and is transforming industries. This article will discuss new emerging technologies that are being developed for all areas of HPC: compute/processing, memory and storage, interconnect fabric, I/O and software to address the ongoing challenges in HPC such as balanced architecture, energy efficient high-performance, density, reliability, sustainability, and last but not least ease-of-use. Of specific interest are the challenges and opportunities for the next frontier in HPC envisioned around the 2020 timeframe: ExaFlops computing. We will also outline the new and emerging area of High Performance Data Analytics, Big Data Analytics using HPC, and discuss the emerging new delivery mechanism for HPC - HPC in the Cloud.

As the size and complexity of high performance computing (HPC) systems grow in line with advancements in hardware and software technology, HPC systems increasingly suffer from performance variations due to shared resource contention as well as software- and hardware-related problems. Such performance variations can lead to failures and inefficiencies, which impact the cost and resilience of HPC systems. To minimize the impact of performance variations, one must quickly and accurately detect and diagnose the anomalies that cause the variations and take mitigating actions. However, it is difficult to identify anomalies based on the voluminous, high-dimensional, and noisy data collected by system monitoring infrastructures. This paper presents a novel machine learning based framework to automatically diagnose performance anomalies at runtime. Our framework leverages historical resource usage data to extract signatures of previously-observed anomalies. We first convert collected time series data into easy-to-compute statistical features. We then identify the features that are required to detect anomalies, and extract the signatures of these anomalies. At runtime, we use these signatures to diagnose anomalies with negligible overhead. We evaluate our framework using experiments on a real-world HPC supercomputer and demonstrate that our approach successfully identifies 98 percent of injected anomalies and consistently outperforms existing anomaly diagnosis techniques.

The theoretical concept of technological performance has emerged only recently with the publication of Jon McKenzie's Perform or Else: From Discipline to Performance in 2001. McKenzie develops a general theory of performance based around the development of three performance paradigms: cultural performance, organizational performance (or performance management), and technological performance. In his examination of the techno-performance paradigm, he focuses primarily on late twentieth and early twenty-first century 'high performance' technologies such as computers, guided missiles and space shuttles. While he acknowledges that the concept of performance does not apply only to technologies in this period, his analysis implicitly suggests that high performance technologies are a unique invention of the modern age. This essay confronts McKenzie's restriction of techno-performance to the post-WWII period by demonstrating how technologies performed and were seen to perform in the late nineteenth century through a techno-performance reading of Bram Stoker's Dracula (1897). The late Victorian period saw startling innovations in information and communication technology (such as the overseas telegraph, the typewriter, and the gramophone), which were marketed as high performance technologies, though not in those words. To contextualise my reading of Dracula , I examine contemporary Victorian advertisements for communication technologies to demonstrate how such technologies were viewed as high performance products by Victorian advertisers and consumers. Technology in Dracula has usually been read as a metaphor. I employ McKenzie's concept of techno-performance to examine the performative functions of technology in Dracula that have not yet been explored by Victorianist scholarship. McKenzie notes two challenges posed by techno-performance: first, the challenge posed by a developer to his/her technological product, to perform or be classified as obsolete; and, second, the challenge posed by technology to its user to perform or be regarded as outmoded. I argue that Stoker's Dracula takes up both of these challenges. Emergent technologies sometimes perform in unexpected and potentially disruptive ways, much like the space shuttle Challenger cited by McKenzie; at the same time, such technologies oblige their users to perform in unexpected and disruptive ways. This essay examines emergent technologies in Dracula to highlight the relationship between individual and technological performance in the late nineteenth century.

In this paper, the role of optical interconnects in high performance computing systems is explored. A Network-of- Workstations (NOW) consists of a collection of commodity workstations or high-end Personal Computers (PCs) interconnected with a high bandwidth Local Area Network (LAN). Due to technological advances, it has been argued that NOWs can offer cost-effective high performance computing systems, ranging from low-end interactive computing to high-end parallel computing. Over the last decade the industry has been moving in this direction: Commercial high end computing systems such as the Silicon Graphics Origin 2000 multiprocessor support up to 512 dual-processor nodes interconnected with a scalable network. In this paper, we argue that when NOWs are enhanced with a new generation of very high bandwidth optical networks, they can provide supercomputer-class performance at a fraction of the cost. We describe the architectures of ring-based and star-based multi- terabit optical networks for multiprocessor systems, and discuss their impact on system performance. These networks exploit the CMOS/VCSEL optoelectronic integrated circuit technology to yield exceptionally large bandwidths with small form factors and potentially low cost. We view these new optical networks as the key to the future evolution of multiprocessor systems, and we believe that such systems can open a new era of exceptionally high performance computing.

This open access book is the final compendium of case studies emanated from the 4-year COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications” (cHiPSet). Funded by the European Commission from 2015, cHiPSet has created a sustainable reference network linking applied research in High Performance Computing (HPC) and Modelling & Simulation to tangibly address Big Data challenges. cHiPSet has enabled research partnerships for dozens of academics and industry practitioners located in 34 COST countries, as well as in Australia, Belarus, Brazil, China, Russia, and the USA. As a cooperation framework, cHiPSet has reached out to new audiences such as ICT professionals, commercial software developers, and the general public. At a time when Big Data has become a common household term, cHiPSet has strived to become a knowledge hub where data-driven HPC meets Modelling & Simulation. cHiPSet has also endeavoured to use and exploit results through Open Science practices, i.e., open access publication, open access to data repositories, and open-source software development. A testament to this philosophy, this compendium is set to become a required reference for the fast-changing fields of HPC, Big Data, and Modelling & Simulation.

PC cluster systems are becoming popular in the field of high-performance computing. The authors have been studying the electromagnetic field analysis of high T/sub c/ superconductors (HTS) by the finite element method (FEM) for AC loss estimations. Superconductors are highly nonlinear electromagnetic media, and considerable computation time is required to calculate the temporal evolution of the electromagnetic field distribution in superconductors. In this study, parallel computing techniques were applied to the electromagnetic field analysis of HTSs by the finite element method. In one of the FEM codes used by the authors, the successive over-relaxation (SOR) method, was used to solve a system of equations. This part was parallelized using the multicolor SOR method, and it helped in reducing computation time. Data are explicitly passed between the processor elements (PEs) through an MPI. First, the HTS model was analyzed under one operating condition, using the code implemented multicolor SOR method, using four PEs, and the parallelization efficiency was confirmed. Next, the generality of the efficiency of the multicolor SOR method in our numerical simulation was examined under several operating conditions, using different models.

This article delves into the crucial roles of parallelism and asynchrony in the development of high-performance software programs. It provides an insightful exploration into how these methodologies enhance computing systems' efficiency and performance. The discussion begins with an overview of high-performance programs and their importance in modern computing, followed by a detailed analysis of parallelism, including its hardware and software aspects, various programming models, and diverse applications in fields like computational tasks, data processing, and machine learning. The article then shifts focus to asynchrony, elaborating on its implementation through callback functions, promises, and the async/await syntax. It highlights the significant benefits of asynchrony in improving program responsiveness and efficiency, especially in I/O-bound and network-intensive operations. A critical section of the article examines the combined application of parallelism and asynchrony in different domains, notably in Graphics Processing Unit (GPU) computations, distributed systems, and web development. This combination is shown to be particularly effective in handling Big Data, where rapid and efficient data processing is essential. The article concludes by emphasizing the indispensable nature of these approaches in modern software development. Parallelism and asynchrony are not merely performance enhancement tools but are fundamental in building innovative, resilient, and scalable software solutions. This comprehensive exploration underscores the ongoing relevance of these methodologies and their potential to drive further advancements in the field of high-performance computing.