Using remote accelerators to improve the performance of mathematical libraries

[EN] Virtualization technologies, such as virtual machines, have demonstrated to provide economic savings to data centers because they increase overall resource utilization. However, in addition to virtualize entire computers, the virtualization of individual devices also provides significant benefits. As an example, the remote GPU virtualization technique moves local GPU computations to remote GPU devices. Nevertheless, it is also possible to offload the computationally intensive CPU parts of an application to remote accelerators. In this work we present a first implementation of a new middleware that uses remote accelerators to perform the computations of scientific libraries, which were initially intended to be executed in the local CPU. Moreover, forwarding the computationally intensive parts of these libraries to remote accelerators is done in a transparent way to applications, not having to modify their source code. The first implementation of the new middleware is based on offloading the FFT and BLAS libraries, which are used to present the benefits of the new proposal by carrying out an in-depth performance evaluation. Results demonstrate that the new middleware is feasible. Moreover, scientific libraries such as FFTW may experience a speed-up larger than 25x, despite of having to transfer data back and forth to the remote server owning the accelerator.Mislata Valero, S. (2016). Using remote accelerators to improve the performance of mathematical libraries. http://hdl.handle.net/10251/76426Archivo delegad

Next generation of Exascale-class systems: ExaNeSt project and the status of its interconnect and storage development

The ExaNeSt project started on December 2015 and is funded by EU H2020 research framework (call H2020-FETHPC-2014, n. 671553) to study the adoption of low-cost, Linux-based power-efficient 64-bit ARM processors clusters for Exascale-class systems. The ExaNeSt consortium pools partners with industrial and academic research expertise in storage, interconnects and applications that share a vision of an European Exascale-class supercomputer. The common goal is designing and implementing a physical rack prototype together with its cooling system, the non-volatile memory (NVM) architecture and a unified low-latency interconnect able to test different options for network and storage. Furthermore, the consortium goal is to provide real HPC applications to validate the system. In this paper we describe the unified data and storage network architecture, reporting on the status of development of different testbeds and highlighting preliminary benchmark results obtained through the execution of scientific, engineering and data analytics scalable application kernels

Efficient algorithms for the fast computation of space charge effects caused by charged particles in particle accelerators

In this dissertation, a Poisson solver is improved with three parts: the efficient integrated Green's function; the discrete cosine transform of the efficient integrated Green's function values; the implicitly zero-padded fast Fourier transform for charge density. In addition, the high performance computing technology is utilized for the further improvement of efficiency, such as: OpenMP API, OpenMP+CUDA, MPI, and MPI+OpenMP parallelizations. The examples and simulation results are matched with the results of the commonly used Poisson solver to demonstrate the accuracy performance

07361 Abstracts Collection -- Programming Models for Ubiquitous Parallelism

From 02.09. to 07.09.2007, the Dagstuhl Seminar 07361 ``Programming Models for Ubiquitous Parallelism\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Acceleration of Axisymetric Ultrasound Simulations

Simulácia šírenia ultrazvuku prostredníctvom mäkkých biologických tkanív má širokú škálu praktických aplikácií. Patria sem dizajn prevodníkov pre diagnostický a terapeutický ultrazvuk, vývoj nových metód spracovania signálov a zobrazovacích techník, štúdium anomálií ultrazvukových lúčov v heterogénnych médiách, ultrazvuková klasifikácia tkanív, učenie rádiológov používať ultrazvukové zariadenia a interpretáciu ultrazvukových obrazov, modelové vrstvenie medicínskeho obrazu a plánovanie liečby pre ultrazvuk s vysokou intenzitou. Ultrazvuková simulácia však predstavuje výpočtovo zložitý problém, pretože simulačné domény sú veľmi veľké v porovnaní s akustickými vlnovými dĺžkami, ktoré sú predmetom záujmu. Ale ak je problém osovo symetrický, problém môže byť riešený v 2D.To umožňuje spúšťanie simulácií na mriežke s väčším počtom bodov, s menším využitím výpoč- tových zdrojov za kratšiu dobu. Táto práca modeluje a implementuje zrýchlenie vlnovej nelineárnej ultrazvukovej simulácie v axisymetrickom súradnicovom systéme realizovanom v Matlabe pomocou Mex súborov pre diskrétne sínové a kosínové transformácie. Axisymetrická simulácia bola implementovaná v C++ ako open source rozšírenie K-WAVE toolboxu. Kód je optimalizovaný na beh na jednom uzle superpočítaču Salomon (IT4Innovations, Ostrava, Česká republika) s dvoma dvanásť-jadrovými procesormi Intel Xeon E5-2680v3. Na maximalizáciu výpočtovej efektívnosti boli vykonané viaceré optimalizácie kódu. Po prvé, fourierové tramsformácie boli vypočítané pomocou real-to-complex FFT z knižnice FFTW. V porovnaní s complex-to-complex FFT to znížilo čas výpočtu a pamäť spojenú s výpočtom FFT o takmer 50%. Taktiež diskrétne sínové a kosínové transformácie sa počítali pomocou knižnice FFTW, ktoré v Matlab verzii museli byť vyvolané z dynamicky načítaných MEX súborov. Po druhé, aby sa znížilo zaťaženie priepustnosti pamäte, boli všetky operácie počítané jednoduchej presnosti pohyblivej rádovej čiarky. Po tretie, elementárne operá- cie boli paralelizované pomocou OpenMP a potom vektorizované pomocou rozšírení SIMD (SSE). Celkový výpočet C++ verzie je až do 34-násobne rýchlejší a využíva menej ako tretinu pamäte ako Matlab verzia simulácie. Simulácia ktorá by trvala takmer dva dni tak môže byť vypočítaná za jeden a pol hodinu. Toto všetko umožňuje počítať simuláciu na výpočetnej mriežke s veľkosťou 16384 × 8192 bodov v primeranom čase.The simulation of ultrasound propagation through soft biological tissue has a wide range of practical applications. These include the design of transducers for diagnostic and therapeutic ultrasound, the development of new signal processing and imaging techniques, studying the aberration of ultrasound beams in heterogeneous media, ultrasonic tissue classification, training ultrasonographers to use ultrasound equipment and interpret ultrasound images, model-based medical image registration, and treatment planning and dosimetry for high-intensity focused ultrasound. However, ultrasound simulation presents a computationally difficult problem, as simulation domains are very large compared with the acoustic wavelengths of interest. But if the problem is axisymmetric, the governing equations can also be solved in 2D. This allows running simulations with larger grid size, with less computational resources and in a shorter time. This paper model and implements an acceleration of the Full-wave Nonlinear Ultrasound Simulation in an Axisymmetric Coordinate System implemented in Matlab using Mex Files for FFTW DST and DCT transformations. The axisymmetric simulation was implemented in C++ as an extension to the open source K-WAVE toolbox. The codes were optimized to run using one node of Salomon supercomputer cluster (IT4Innovations, Ostrava, Czechia) with two twelve-core Intel Xeon E5-2680v3 processors. To maximize computational efficiency, several stages of code optimization were performed. First, the FFTs were computed using the real-to-complex FFT from the FFTW library. Compared to the complex-to-complex FFT, this reduced the compute time and memory associated with the FFT by nearly 50%. Also, real-to-real DCTs and DSTs were computed using FFTW library, which ones in Matlab version, had to be invoked from dynamically loaded MEX Files. Second, to save memory bandwidth, all operations were computed in single precision. Third, element-wise operations were parallelized using OpenMP and then optimized using streaming SIMD extensions (SSE). The overall computation of the C++ k-space model is up to 34-times faster and uses less than one-third of the memory than Matlab version. The simulation which would take nearly two days by Matlab implementation can be now computed in one and half hour. This all allows running the simulation on the computational grid with 16384 × 8192 grid points within a reasonable time.

Energy reconstruction on the LHC ATLAS TileCal upgraded front end: feasibility study for a sROD co-processing unit

Dissertation presented in ful lment of the requirements for the degree of: Master of Science in Physics 2016The Phase-II upgrade of the Large Hadron Collider at CERN in the early 2020s will enable an order of magnitude increase in the data produced, unlocking the potential for new physics discoveries. In the ATLAS detector, the upgraded Hadronic Tile Calorimeter (TileCal) Phase-II front end read out system is currently being prototyped to handle a total data throughput of 5.1 TB/s, from the current 20.4 GB/s. The FPGA based Super Read Out Driver (sROD) prototype must perform an energy reconstruction algorithm on 2.88 GB/s raw data, or 275 million events per second. Due to the very high level of pro ciency required and time consuming nature of FPGA rmware development, it may be more e ective to implement certain complex energy reconstruction and monitoring algorithms on a general purpose, CPU based sROD co-processor. Hence, the feasibility of a general purpose ARM System on Chip based co-processing unit (PU) for the sROD is determined in this work. A PCI-Express test platform was designed and constructed to link two ARM Cortex-A9 SoCs via their PCI-Express Gen-2 x1 interfaces. Test results indicate that the latency of the PCI-Express interface is su ciently low and the data throughput is superior to that of alternative interfaces such as Ethernet, for use as an interconnect for the SoCs to the sROD. CPU performance benchmarks were performed on ve ARM development platforms to determine the CPU integer, oating point and memory system performance as well as energy e ciency. To complement the benchmarks, Fast Fourier Transform and Optimal Filtering (OF) applications were also tested. Based on the test results, in order for the PU to process 275 million events per second with OF, within the 6 s timing budget of the ATLAS triggering system, a cluster of three Tegra-K1, Cortex-A15 SoCs connected to the sROD via a Gen-2 x8 PCI-Express interface would be suitable. A high level design for the PU is proposed which surpasses the requirements for the sROD co-processor and can also be used in a general purpose, high data throughput system, with 80 Gb/s Ethernet and 15 GB/s PCI-Express throughput, using four X-Gene SoCs

Radio-Astronomical Imaging on Accelerators

Imaging is considered the most compute-intensive and therefore most challenging part of a radio-astronomical data-processing pipeline. To reach the high dynamic ranges imposed by the high sensitivity and large field of view of the new generation of radio telescopes such as the Square Kilometre Array (SKA), we need to be able to correct for direction-independent effects (DIEs) such as the curvature of the earth as well as for direction-dependent time-varying effects (DDEs) such as those caused by the ionosphere during imaging. The novel Image-Domain gridding (IDG) algorithm was designed to avoid the performance bottlenecks of traditional imaging algorithms. We implement, optimize, and analyze the performance and energy efficiency of IDG on a variety of hardware platforms to find which platform is the best for IDG. We analyze traditional CPUs, as well as several accelerators architectures. IDG alleviates the limitations of traditional imaging algorithms while it enables the advantages of GPU acceleration: better performance at lower power consumption. The hardware-software co-design has resulted in a highly efficient imager. This makes IDG on GPUs an ideal candidate for meeting the computational and energy efficiency constraints of the SKA. IDG has been integrated with a widely-used astronomical imager (WSClean) and is now being used in production by a variety of different radio observatories such as LOFAR and the MWA. It is not only faster and more energy-efficient than its competitors, but it also produces better quality images