FPGA-based Stream Computing for High-Performance N-Body Simulation using Floating-Point DSP Blocks

Sano, Kentaro; Abiko, Satoko; Ueno, Tomohiro

doi:10.1145/3120895.3120909

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…As the porting of the OmpSs@FPGA framework to the Alveo boards is still an ongoing work, this number is expected to grow as a more mature implementation that allows a more thorough testing is developed. This value of 57.67 bapps can be compared with 11 bapps obtained by Sano et al [ 9 ] for force calculations with using cut-off distances (although we are talking about different technologies, Alveo U200 vs. Arria 10 with more than 5 years difference). A more direct comparison can be made on the histogram construction by A. Leonardi and D.L.…”

Section: Resultsmentioning

confidence: 56%

“…This demonstrates that current FPGA boards can provide good results for these kinds of O(N 2 ) problems. In this context, Sano et al [ 9 ] obtained the best results, for a single FPGA, on the Arria 10, with a score of 10.94 billion force calculations per second for 262,144 particles, at 180 MHz kernel frequency. A direct comparison is not possible because Sano et al [ 9 ] not only computed the distance between all the pairs, but also this information was further used for the calculation of the total force experienced by each body.…”

Section: Introductionmentioning

confidence: 99%

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High Performance Computing PP-Distance Algorithms to Generate X-ray Spectra from 3D Models

González

Balocco

Bosch

et al. 2022

IJMS

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X-ray crystallography is a powerful method that has significantly contributed to our understanding of the biological function of proteins and other molecules. This method relies on the production of crystals that, however, are usually a bottleneck in the process. For some molecules, no crystallization has been achieved or insufficient crystals were obtained. Some other systems do not crystallize at all, such as nanoparticles which, because of their dimensions, cannot be treated by the usual crystallographic methods. To solve this, whole pair distribution function has been proposed to bridge the gap between Bragg and Debye scattering theories. To execute a fitting, the spectra of several different constructs, composed of millions of particles each, should be computed using a particle–pair or particle–particle (pp) distance algorithm. Using this computation as a test bench for current field-programmable gate array (FPGA) technology, we evaluate how the parallel computation capability of FPGAs can be exploited to reduce the computation time. We present two different solutions to the problem using two state-of-the-art FPGA technologies. In the first one, the main C program uses OmpSs (a high-level programming model developed at the Barcelona Supercomputing Center, that enables task offload to different high-performance computing devices) for task invocation, and kernels are built with OpenCL using reduced data sizes to save transmission time. The second approach uses task and data parallelism to operate on data locally and update data globally in a decoupled task. Benchmarks have been evaluated over an Intel D5005 Programmable Acceleration Card, computing a model of 2 million particles in 81.57 s – 24.5 billion atom pairs per second (bapps)– and over a ZU102 in 115.31 s. In our last test, over an up-to-date Alveo U200 board, the computation lasted for 34.68 s (57.67 bapps). In this study, we analyze the results in relation to the classic terms of speed-up and efficiency and give hints for future improvements focused on reducing the global job time.

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Section: Resultsmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

High Performance Computing PP-Distance Algorithms to Generate X-ray Spectra from 3D Models

González

Balocco

Bosch

et al. 2022

IJMS

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“…There have been other efforts to implement the N-body on FPGAs and even ASICS. In [10], Sano et al implement a full custom FPGA design that solves the N-body problem on a single Intel Arria10 FPGA, achieving 10.944 Gpairs/s. In [6], de Haro et al uses OmpSs@FPGA to execute the N-body on a Xilinx Alveo U200 board reaching 37.62 Gpairs/s with a performance per watt of 0.58.…”

Section: Related Workmentioning

confidence: 99%

OmpSs@cloudFPGA: An FPGA Task-Based Programming Model with Message Passing

Haro

Cano

Álvarez

et al. 2022

2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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Nowadays, a new parallel paradigm for energyefficient heterogeneous hardware infrastructures is required to achieve better performance at a reasonable cost on highperformance computing applications. Under this new paradigm, some application parts are offloaded to specialized accelerators that run faster or are more energy-efficient than CPUs. Field-Programmable Gate Arrays (FPGA) are one of those types of accelerators that are becoming widely available in data centers.This paper proposes OmpSs@cloudFPGA, which includes novel extensions to parallel task-based programming models that enable easy and efficient programming of heterogeneous clusters with FPGAs. The programmer only needs to annotate, with OpenMP-like pragmas, the tasks of the application that should be accelerated in the cluster of FPGAs. Next, the proposed programming model framework automatically extracts parts annotated with High-Level Synthesis (HLS) pragmas and synthesizes them into hardware accelerator cores for FPGAs. Additionally, our extensions include and support two novel features: 1) FPGA-to-FPGA direct communication using a Message Passing Interface (MPI) similar Application Programming Interface (API) with one-to-one and collective communications to alleviate host communication channel bottleneck, and 2) creating and spawning work from inside the FPGAs to their own accelerator cores based on an MPI rank-like identification. These features break the classical host-accelerator model, where the host (typically the CPU) generates all the work and distributes it to each accelerator.We also present an evaluation of OmpSs@cloudFPGA for different parallel strategies of the N-Body application on the IBM cloudFPGA research platform. Results show that for cluster sizes up to 56 FPGAs, the performance scales linearly. To the best of our knowledge, this is the best performance obtained for N-body over FPGA platforms, reaching 344 Gpairs/s with 56 FPGAs. Finally, we compare the performance and power consumption of the proposed approach with the ones obtained by a classical execution on the MareNostrum 4 supercomputer, demonstrating that our FPGA approach reduces power consumption by an order of magnitude.

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“…However, they only propose a design for the force calculation, and do not take into account more than one simulation step, which requires to update the positions and velocities. In [12] Sano et. al.…”

Section: N-bodymentioning

confidence: 99%

OmpSs@FPGA framework for high performance FPGA computing

Haro

Bosch

Filgueras

et al. 2021

IEEE Trans. Comput.

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This paper presents the new features of the OmpSs@FPGA framework. OmpSs is a data-flow programming model that supports task nesting and dependencies to target asynchronous parallelism and heterogeneity. OmpSs@FPGA is the extension of the programming model addressed specifically to FPGAs. OmpSs environment is built on top of Mercurium source to source compiler and Nanos++ runtime system. To address FPGA specifics Mercurium compiler implements several FPGA related features as local variable caching, wide memory accesses or accelerator replication. In addition, part of the Nanos++ runtime has been ported to hardware. Driven by the compiler this new hardware runtime adds new features to FPGA codes, such as task creation and dependence management, providing both performance increases and ease of programming. To demonstrate these new capabilities, different high performance benchmarks have been evaluated over different FPGA platforms using the OmpSs programming model. The results demonstrate that programs that use the OmpSs programming model achieve very competitive performance with low to moderate porting effort compared to other FPGA implementations.

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FPGA-based Stream Computing for High-Performance N-Body Simulation using Floating-Point DSP Blocks

Cited by 9 publications

References 9 publications

High Performance Computing PP-Distance Algorithms to Generate X-ray Spectra from 3D Models

High Performance Computing PP-Distance Algorithms to Generate X-ray Spectra from 3D Models

OmpSs@cloudFPGA: An FPGA Task-Based Programming Model with Message Passing

OmpSs@FPGA framework for high performance FPGA computing

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