HPP: An Architecture for High Performance and Utility Computing

A 3D Free Surface Flow and Transport Model on Different High Performance Computation Architectures

At first thought, one may not realize the need for a special issue of Parallel and Distributed Computing Practices focused on Cluster Computing when our community has already invested years of research and spent a wealth of resources on traditional Big Iron supercomputers, for instance, the SGI/Cray Origin and IBM SP. Academia, industry, and national laboratories are still using, and for the foreseeable future, will continue to use, supercomputers to solve both grand-challenge scale and high-throughput applications. Then why the interest and new popularity of research into clusters for high-performance parallel and distributed applications? Clusters incorporate the tremendous successes that are attributable to our parallel and distributed processing community. Over the next decade, clusters will span the entire range of high-performance computing platforms. Instead of being restricted solely to supercomputers for achieving high-performance computing, the spectrum of available platforms will spread out to Beowulf-class clusters, to superclusters, and on to supercomputers. Moving along this continuum represents increasing computational resources, which in turn allows the capability for solving larger classes of applications or instances of problems. Compute-bound applications on workstations often may take advantage of the extra horsepower provided by symmetric multiprocessors. Applications with even larger computational requirements and a high-degree of concurrency may use Beowulf clusters, that is, clusters that are home-built from mass-market, commercial off-the-shelf workstations and networks, for instance, PC's interconnected by Fast Ethernet. A supercluster may contain commodity SMP nodes interconnected with scalable, low-latency, high-bandwidth networks, and is integrated by a system vendor rather than the computational scientist. Applications with greater complexity, for example, grand challenges from the sciences, demand scalable systems with high-performance, interconnects, perhaps coupled with terascale I/O subsystems, and thus, are appropriate for these superclusters. Finally, supercomputers, the largest of the clusters, may provide the ultimate platform for the highest-fidelity models and simulations and data-intensive applications. At each step up this ladder of cluster architectures, the system cost increases approximately by an order of magnitude, but having the high-performance capability to solve the problem of interest may require an entry point at any of these levels, the Beowulf, or supercluster, or even supercomputer platform. Cluster computing with Beowulf- or superclusters, thus, may be one or two orders of magnitude less expensive than a supercomputer, and provide a cost-effective solution and capability that was not available on a workstation. But this cost comparison measures just the basic system price. What about the applications? As recently as a few years ago, the lack of a canonical high-performance architectural paradigm meant that migrating to a new computer system, even from the same vendor, typically required redesigning applications to efficiently use the high-performance system. Thus, the application must be redesigned with parallel algorithms and libraries that are optimized for each high-performance system. This process of porting, redesigning, optimizing, debugging, and analyzing, the application may in fact be prohibitively expensive, and certainly frustrating when the process is completed just in time to greet the next generation system! That was then, this is now. With the successful standardization of system-level interfaces, many of these issues no longer hinder our expedited use of these new, high-performance clusters. The message-passing interface (MPI) is a clear example of a standard that has contributed significantly to the success of cluster computing. Supercomputer vendors working together with the high-performance computing community embarked on a process to standardize the user-level interface and behavior for passing messages between concurrent tasks. Now, using ordinary sequential compilers and vendor-supplied or freely-available implementations of this message-passing interface, applications can be implemented efficiently on one supercomputer and ported with little or no effort to other parallel machines. The message-passing paradigm, thus, provides a reasonably good canonical high-performance model and methodology for implementing high-performance applications. An algorithm that exhibits efficient execution and scaling on one machine then is likely to perform well on another with little or no additional programming effort. Using this cluster computing paradigm, algorithms typically are optimized by minimizing the number of remote memory accesses (reducing the amount of messages passed), and carefully organizing the data placement to increase memory locality. Thus, high-performance algorithms and applications that have been optimized for supercomputers, using the same cluster paradigm, may be appropriate for Beowulf and superclusters. Cluster computing, realizing the importance of a standard high-performance programming environment that applies equally well to both ends of the architectural spectrum, highly leverages these and other successes from parallel and distributed processing research and development. This is in an exciting, new era for high-performance computing where the canonical architectural paradigm stretches from home-built Beowulf clusters, to superclusters, and on to supercomputers. But cluster computing faces some challenges ahead of it. Continued research is required for designing 1) scalable, high-performance algorithms, applications, and methodologies, that can tolerate the communication overheads, and adapt to hybrid, SMP, and heterogeneous, nodes; 2) high-performance computer architectures, scalable interconnection networks, and I/O subsystems; and 3) scalable systems software, compilers, and monitoring tools. Cluster computing has come of age, and it is high time for the parallel and distributed processing community to lay the groundwork for our own legacy of success stories. David A. Bader University of New Mexico

Stacked DRAM memories have become a reality in High-Performance Computing (HPC) architectures. These memories provide much higher bandwidth while consuming less power than traditional off-chip memories, but their limited memory capacity is insufficient for modern HPC systems. For this reason, both stacked DRAM and off-chip memories are expected to co-exist in HPC architectures, giving raise to different approaches for architecting the stacked DRAM in the system. This paper proposes a runtime approach to transparently manage stacked DRAM memories in task-based programming models. In this approach the runtime system is in charge of copying the data accessed by the tasks to the stacked DRAM, without any complex hardware support nor modifications to the application code. To mitigate the cost of copying data between the stacked DRAM and the off-chip memory, the proposal includes an optimization to parallelize the copies across idle or additional helper threads. In addition, the runtime system is aware of the reuse pattern of the data accessed by the tasks, and can exploit this information to avoid unworthy copies of data to the stacked DRAM. Results on the Intel Knights Landing processor show that the proposed techniques achieve an average speedup of 14% against the state-of-the-art library to manage the stacked DRAM and 29% against a stacked DRAM architected as a hardware cache.

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.

Modeling of containment structures on high performance computers

GriF is a Workflow Management System designed to support users of the Molecular and Materials Sciences and Technologies COMPCHEM Virtual Organization. In particular, GriF manages the results of the parameter sweeping studies submitted to the Grid. This service has been recently extended to implement seamless access to both High Performance and High Throughput Computing architectures. To this end, the Workflow execution is splitted into single blocks whose execution is most suited to one or the other platform. This has been obtained by implementing a communication channel allowing interoperability between the gLite middleware operating on the Italian Grid Initiative segment accessible to COMPCHEM and the software stack operating on the CINECA machines. As a prototype application a high level ab initio calculation of the potential energy surface of few atom system has been implemented.

gem5 has been extensively used in computer architecture simulations and in the evaluation of new architectures for HPC (high performance computing) systems. Previous work has validated gem5 against ARM platforms. However, gem5 still shows high inaccuracy when modeling Intel’s x86 based processors. In this work, we focus on the simulation of a single node high performance system and study the sources of inaccuracies of gem5. Then we validate gem5 simulator against an Intel processor, Core-i7 (Haswell microarchitecture). We configured gem5 as close as possible to match Core-i7 Haswell microarchitecture configurations and made changes to the simulator to add some features, modified existing code, and tuned built-in configurations. As a result, we validated the simulator by fixing many sources of errors to match real hardware results with less than 6% mean error rate for different control, memory, dependency and execution microbenchmarks.

With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end.

Contemporary High Performance Computing: From Petascale toward Exascale focuses on the ecosystems surrounding the worlds leading centers for high performance computing (HPC). It covers many of the important factors involved in each ecosystem: computer architectures, software, applications, facilities, and sponsors. The first part of the book examines significant trends in HPC systems, including computer architectures, applications, performance, and software. It discusses the growth from terascale to petascale computing and the influence of the TOP500 and Green500 lists. The second part of the book provides a comprehensive overview of 18 HPC ecosystems from around the world. Each chapter in this section describes programmatic motivation for HPC and their important applications; a flagship HPC system overview covering computer architecture, system software, programming systems, storage, visualization, and analytics support; and an overview of their data center/facility. The last part of the book addresses the role of clouds and grids in HPC, including chapters on the Magellan, FutureGrid, and LLGrid projects. With contributions from top researchers directly involved in designing, deploying, and using these supercomputing systems, this book captures a global picture of the state of the art in HPC.

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.

The High Performance Computing Modernization Program (HPCMP) obtains new supercomputing hardware each year through a rigorous acquisition process known as technology insertion (TI). TI is based primarily on application benchmarks. The suite of applications used spans many disciplines and is carefully constructed to accurately reflect the high Performance Computing Modernization Program (HPCMP) computational workload. The application codes GAMESS, OOCORE, and RF-CTH were used to represent the contributions to the workload in the areas of computational chemistry and materials science (CCM), computational electromagnetics and acoustics (CEA), and computational structural mechanics (CSM), respectively. This paper will compare the performance of the application test cases GAMESS standard/large, OOCORE standard/large, and RF-CTH standard/large on major HPCMP high performance computing (HPC) architectures.

A large percentage of colleges and universities in the world do not have a HPC culture existing on campus, where access to supercomputing resources (hardware) or supercomputing personnel (faculty and technical staff) and the need to work on grand challenge applications (GCA) is considerably absent. Incorporating courses related to HPC in such institutions presents multiple challenges - non-availability of HPC resources, academic staff trained in HPC related areas, inertia faced in changing the curriculum, absence of a tangible local industry, paucity of funds, lack of enthusiasm among academicians and perceived ignorance. This paper presents a classification of institutions based on three HPC related criteria and highlights the challenges faced in introducing HPC related courses into institutions that do not have a HPC culture. Experiences related to introducing HPC related topics in two institutions, PACE in India and KKU in Saudi Arabia, falling in two different regions in the classification, have been highlighted.

In the effort for exascale performance, current High-Performance Computing (HPC) systems need to be improved. Simple hardware scaling is not a feasible solution due to the increasing utility costs and power consumption limitations. Hence, apart from improvements in implementation technology, what is needed is to refine the HPC application development flow as well as the system architecture of future HPC systems. ECOSCALE tackles precisely these challenges by proposing a scalable programming environment and architecture, aiming to substantially reduce energy consumption as well as data traffic and latency. Furthermore, ECOSCALE introduces a novel heterogeneous energy-efficient hierarchical architecture, as well as a hybrid many-core+OpenCL programming environment and runtime system. The ECOSCALE approach is hierarchical and is expected to scale well by partitioning the physical system into multiple independent Workers. Workers are interconnected in a tree-like fashion and define a contiguous global address space that can be viewed either as a set of partitions in a Partitioned Global Address Space (PGAS), or as a set of nodes hierarchically interconnected via an MPI-like protocol. To further increase energy efficiency, as well as to provide resilience, the Workers employ reconfigurable accelerators mapped onto the virtual address space by utilising a dual-stage system memory management unit with coherent memory access. The architecture supports shared partitioned reconfigurable resources accessed by any Worker in a PGAS partition, as well as automated hardware synthesis of these resources from an OpenCL-based programming model.

R is a robust open-source programming language mainly used for statistical computing . Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel/concurrent computing. This paper presents an overview of techniques for parallel computing with R on ACI (a PSU Infrastructure) and benchmark it with C/C++. We review the scalabilty concern of R, and look at the simplicity of using R as a primary language in Coding for HPC. We will look at the various R packages for HPC like Rmpi, Rcpp, snow and snowfall. We utilize a series of algorithms to benchmark and will illustrate each benchmark with a representative graph for ease of understanding. The paper concludes with a better understanding of which language to use when in high performance computing .

The transition towards net-zero emissions is inevitable for humanity’s future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind and solar-powered resources into future power grids. Additionally, new paradigms such as large-scale integration of distributed resources into the grid, proliferation of Internet of Things (IoT) technologies, and electrification of different sectors are envisioned as essential enablers for a net-zero future. However, these changes will lead to unprecedented size, complexity and data of the planning and operation problems of future grids. It is thus important to discuss and consider High Performance Computing (HPC), parallel computing, and cloud computing prospects in any future electrical energy studies. This article recounts the dawn of parallel computation in power system studies, providing a thorough history and paradigm background for the reader, leading to the most impactful recent contributions. The reviews are split into Central Processing Unit (CPU) based, Graphical Processing Unit (GPU) based, and Cloud-based studies and smart grid applications. The state-of-the-art is also discussed, highlighting the issue of standardization and the future of the field. The reviewed papers are predominantly focused on classical imperishable electrical system problems. This indicates the need for further research on parallel and HPC approaches applied to future smarter grid challenges, particularly to the integration of renewable energy into the smart grid.