The transprecision computing paradigm: Concept, design, and applications

Guaranteed numerical precision of each elementary step in a complex computation has been the mainstay of traditional computing systems for many years. This era, fueled by Moore’s law and the constant exponential improvement in computing efficiency, is at its twilight: from tiny nodes of the Internet-of-Things, to large HPC computing centers, subpicoJoule/operation energy efficiency is essential for practical realizations. To overcome the power wall, a shift from traditional computing paradigms is now mandatory. In this paper we present the driving motivations, roadmap, and expected impact of the European project OPRECOMP. OPRECOMP aims to (i) develop the first complete transprecision computing framework, (ii) apply it to a wide range of hardware platforms, from the sub-milliWatt up to the MegaWatt range, and (iii) demonstrate impact in a wide range of computational domains, spanning IoT, Big Data Analytics, Deep Learning, and HPC simulations. By combining together into a seamless design transprecision advances in devices, circuits, software tools, and algorithms, we expect to achieve major energy efficiency improvements, even when there is no freedom to relax end-to-end application quality of results. Indeed, OPRECOMP aims at demolishing the ultraconservative “precise” computing abstraction, replacing it with a more flexible and efficient one, namely transprecision computing

Large-scale performance of a DSL-based multi-block structured-mesh application for Direct Numerical Simulation

SBLI (Shock-wave/Boundary-layer Interaction) is a large-scale Computational Fluid Dynamics (CFD) application, developed over 20 years at the University of Southampton and extensively used within the UK Turbulence Consortium. It is capable of performing Direct Numerical Simulations (DNS) or Large Eddy Simulation (LES) of shock-wave/boundary-layer interaction problems over highly detailed multi-block structured mesh geometries. SBLI presents major challenges in data organization and movement that need to be overcome for continued high performance on emerging massively parallel hardware platforms. In this paper we present research in achieving this goal through the OPS embedded domain-specific language. OPS targets the domain of multi-block structured mesh applications. It provides an API embedded in C/C++ and Fortran and makes use of automatic code generation and compilation to produce executables capable of running on a range of parallel hardware systems. The core functionality of SBLI is captured using a new framework called OpenSBLI which enables a developer to declare the partial differential equations using Einstein notation and then automatically carryout discretization and generation of OPS (C/C++) API code. OPS is then used to automatically generate a wide range of parallel implementations. Using this multi-layered abstractions approach we demonstrate how new opportunities for further optimizations can be gained, such as fine-tuning the computation intensity and reducing data movement and apply them automatically. Performance results demonstrate there is no performance loss due to the high-level development strategy with OPS and OpenSBLI, with performance matching or exceeding the hand-tuned original code on all CPU nodes tested. The data movement optimizations provide over 3× speedups on CPU nodes, while GPUs provide 5× speedups over the best performing CPU node. The OPS generated parallel code also demonstrates excellent scalability on nearly 100K cores on a Cray XC30 (ARCHER at EPCC) and on over 4K GPUs on a CrayXK7 (Titan at ORNL)

Productivity, performance, and portability for computational fluid dynamics applications

Hardware trends over the last decade show increasing complexity and heterogeneity in high performance computing architectures, which presents developers of CFD applications with three key challenges; the need for achieving good performance, being able to utilise current and future hardware by being portable, and doing so in a productive manner. These three appear to contradict each other when using traditional programming approaches, but in recent years, several strategies such as template libraries and Domain Specific Languages have emerged as a potential solution; by giving up generality and focusing on a narrower domain of problems, all three can be achieved. This paper gives an overview of the state-of-the-art for delivering performance, portability, and productivity to CFD applications, ranging from high-level libraries that allow the symbolic description of PDEs to low-level techniques that target individual algorithmic patterns. We discuss advantages and challenges in using each approach, and review the performance benchmarking literature that compares implementations for hardware architectures and their programming methods, giving an overview of key applications and their comparative performance

Using an Adaptive HPC Runtime System to Reconfigure the Cache Hierarchy

The cache hierarchy often consumes a large portion of a processor’s energy. To save energy in HPC environments, this paper proposes software-controlled reconfiguration of the cache hierarchy with an adaptive runtime system. Our approach addresses the two major limitations associated with other methods that reconfigure the caches: predicting the application’s future and finding the best cache hierarchy configuration. Our approach uses formal language theory to express the application’s pattern and help predict its future. Furthermore, it uses the prevalent Single Program Multiple Data (SPMD) model of HPC codes to find the best configuration in parallel quickly. Our experiments using cycle-level simulations indicate that 67 % of the cache energy can be saved with only a 2.4 % performance penalty on average. Moreover, we demonstrate that, for some applica-tions, switching to a software-controlled reconfigurable streaming buffer configuration can improve performance by up to 30 % and save 75 % of the cache energy. I

The Mont-Blanc prototype: an alternative approach for high-performance computing systems

High-performance computing (HPC) is recognized as one of the pillars for further advance of science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging challenges in order to reach Exascale level of performance,which is expected by the year 2020. The much larger embedded and mobile market allows for rapid development of IP blocks, and provides more flexibility in designing an application-specific SoC, in turn giving possibility in balancing performance, energy-efficiency and cost. In the Mont-Blanc project, we advocate for HPC systems be built from such commodity IP blocks, currently used in embedded and mobile SoCs. As a first demonstrator of such approach, we present the Mont-Blanc prototype; the first HPC system built with commodity SoCs, memories, and NICs from the embedded and mobile domain, and off-the-shelf HPC networking, storage, cooling and integration solutions. We present the system’s architecture, and evaluation including both performance and energy efficiency. Further, we compare the system’s abilities against a production level supercomputer. At the end, we discuss parallel scalability, and estimate the maximum scalability point of this approach across a set of HPC applications.Postprint (published version