By Christian de Schryver, Henning Marxen, Stefan Weithoffer, Norbert Wehn (auth.), Wim Vanderbauwhede, Khaled Benkrid (eds.)
High-Performance Computing utilizing FPGA covers the world of excessive functionality reconfigurable computing (HPRC). This ebook offers an summary of architectures, instruments and purposes for High-Performance Reconfigurable Computing (HPRC). FPGAs provide very excessive I/O bandwidth and fine-grained, customized and versatile parallelism and with the ever-increasing computational wishes coupled with the frequency/power wall, the expanding adulthood and services of FPGAs, and the appearance of multicore processors which has brought on the recognition of parallel computational types. The half on architectures will introduce various FPGA-based HPC structures: hooked up co-processor HPRC architectures similar to the CHREC’s Novo-G and EPCC’s Maxwell platforms; tightly coupled HRPC architectures, e.g. the express hybrid-core computing device; reconfigurably networked HPRC architectures, e.g. the QPACE approach, and standalone HPRC architectures similar to EPFL’s CONFETTI approach. The half on instruments will specialise in high-level programming methods for HPRC, with chapters on C-to-Gate instruments (such as Impulse-C, AutoESL, Handel-C, MORA-C++); Graphical instruments (MATLAB-Simulink, NI LabVIEW); Domain-specific languages, languages for heterogeneous computing(for instance OpenCL, Microsoft’s Kiwi and Alchemy projects). The half on functions will current case from a number of software domain names the place HPRC has been used effectively, resembling Bioinformatics and Computational Biology; monetary Computing; Stencil computations; details retrieval; Lattice QCD; Astrophysics simulations; climate and weather modeling.
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Extra resources for High-Performance Computing Using FPGAs
Based on the Eqs. (8) and (9) we have to read two numbers from the BlockRAM at step 2 each clock cycle. To pipeline the random number generator, two block RAMs are needed: one is for xl and the other is for xu . Note that we can pre-compute the parameters and hardwire them to each Mersenne Twister core. Sobol Random Number Generator The theory of Sobol numbers starts with modular integer arithmetic. e. Δ i = j mod m (14) if and only if the difference i − j is divisible by m. For m being prime, the combination of addition and multiplication modulo m, plus a neutral element with 48 X.
2 − 2mant . The bw bw bw output exponent part contains the number of leading zeros in its corresponding input section. If the input exponent section contains only zeros, another sample is taken and the number of zeros is accumulated, until a one occurs or the independently adjustable output range is exceeded. Thus, we can create arbitrary output precision with our approach, not relying on fixed uniform input bit vector sizes. Any uniform RNG can be used to generate inputs for this floating point converter unit.
Nishimura, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simulat. 8(1), 3–30 (1998). 272995 18. O. Mencer, E. Vynckier, J. Spooner, S. Girdlestone, O. Charlesworth, Finding the right level of abstraction for minimizing operational expenditure, in 2011 IEEE Workshop on High Performance Computational Finance (WHPCF) (ACM, New York, USA, 2011), ISBN: 9781-4244-9061-5 19. C. Merton, Theory of rational option pricing. Bell J. Econ.
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