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Architectural Comparisons for a Quantum Monte Carlo Application

Akila Gothandaraman, Rick Weber, Gregory D. Peterson, Robert J. Hinde, Robert J. Harrison

Published Jan 1, 2011• 1 citation
OpenAlex

Abstract

Recent technological advances have led to a number of emerging platforms such as multi-cores, reconfigurable computing, and graphics processing units. We present a comparative study of multi-cores, field-programmable gate arrays, and graphics processing units for a Quantum Monte Carlo chemistry application. The speedups of these implementations are measured relative to a multi-core implementation and the accuracy of each implementation compared against the double-precision CPU implementation. The Brook+ AMD implementation shows the best overall speedup and accuracy, with the mixed-precision NVIDIA CUDA implementation outperforming the Brook+ implementation for larger atomic clusters with a slightly lower accuracy than the Brook+ and FPGA implementations. KeywordsQuantum Monte Carlo, Field-Programmable Gate Arrays, Multi-core processors, Graphics Processing Units.

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•Mar 13, 2020•0 citations
Recent technological advances have led to a number of emerging platforms such as multi-cores, reconfigurable computing, and graphics processing units. We present a comparative study of multi-cores, field-programmable gate arrays, and graphics processing units for a Quantum Monte Carlo chemistry application. The speedups of these implementations are measured relative to a multi-core implementation and the accuracy of each implementation compared against the double-precision CPU implementation. The Brook+ AMD implementation shows the best overall speedup and accuracy, with the mixed-precision NVIDIA CUDA implementation outperforming the Brook+ implementation for larger atomic clusters with a slightly lower accuracy than the Brook+ and FPGA implementations. KeywordsQuantum Monte Carlo, Field-Programmable Gate Arrays, Multi-core processors, Graphics Processing Units.
Abstract A new implementation of many-body calculations is of paramount importance in the field of computational physics. In this study, we leverage the capabilities of Field Programmable Gate Arrays (FPGAs) for conducting quantum many-body calculations. Through the design of appropriate schemes for Monte Carlo and tensor network methods, we effectively utilize the parallel processing capabilities provided by FPGAs. This has resulted in a remarkable tenfold speedup compared to CPU-based computation for a Monte Carlo algorithm. We also demonstrate, for the first time, the utilization of FPGA to accelerate a typical tensor network algorithm. Our findings unambiguously highlight the significant advantages of hardware implementation and pave the way for novel approaches to many-body calculations.
We present the first application of field programmable gate arrays (FPGAs) as new, customizable hardware architectures for carrying out fast and energy-efficient quantum dynamics simulations of large chemical/material systems. Instead of tailoring the software to fixed hardware, which is the typical case for writing quantum chemistry code for central processing units (CPUs) and graphics processing units (GPUs), FPGAs allow us to directly customize the underlying hardware (even at the level of specific electrical signals in the circuit) to give a truly optimized computational performance for quantum dynamics calculations. By offloading the most intensive and repetitive calculations onto an FPGA, we show that the computational performance of our real-time electron dynamics calculations can even exceed that of optimized commercial mathematical libraries running on high-performance GPUs. In addition to this impressive computational speedup, we show that FPGAs are immensely energy-efficient and consume 4 times less energy than modern GPU or CPU architectures. These energy savings are a practical and important metric for supercomputing centers (many of which exceed over $1 million in power costs alone), as exascale computing capabilities become more widespread and commonplace. Taken together, the implementation techniques and performance metrics of our study demonstrate that FPGAs could play a promising role in upcoming quantum chemistry and materials science applications, particularly for the acceleration and energy-efficient execution of quantum dynamics calculations.
The ground-state properties of atomic and molecular clusters can be obtained using Quantum Monte Carlo (QMC) simulations. We propose a reconfigurable hardware architecture using Field-Programmable Gate Arrays (FPGAs) to implement the kernels of the QMC application. To achieve higher clock rates, we experiment with different pipeline stages for each component of the design and develop a deeply pipelined architecture that provides the best performance in terms of clock rate, while at the same time has a modest use of embedded memory and multiplier resources so we can fit additional functions in a future implementation. Here, we discuss the details of the pipelined architecture and our design decisions while developing a general framework that can be used to obtain the potential energy of atomic or molecular clusters and extended to compute other useful properties.
Abstract Massively parallel architectures offer the potential to significantly accelerate an application relative to their serial counterparts. However, not all applications exhibit an adequate level of data and/or task parallelism to exploit such platforms. Furthermore, the power consumption associated with these forms of computation renders “scaling out” for exascale levels of performance incompatible with modern sustainable energy policies. In this work, we investigate the potential for field‐programmable gate arrays (FPGAs) to feature in future exascale platforms, and their capacity to improve performance per unit power measurements for the purposes of scientific computing. We have focused our efforts on variational Monte Carlo, and report on the benefits of coprocessing with a FPGA relative to a purely multicore system.
Abstract: Quantum computers aim to achieve a huge reduction of the time required for solving problems with an exponential complexity, but their simulation in conventional computers results itself on a problem with a similar complexity. As this limits considerably the dimensions of the quantum computer we can simulate, multiprocessor architectures are an almost obliged tool when tackling with such simulations. In this paper we explore the application of the new graphical processor architectures in the simulation of the elementary operators that constitute the basic building blocks of quantum computers. Recently, these GPUs are being used as general purpose multiprocessors, as they demonstrate to possess a floating point computing power larger than classic CPUs. In particular our implementation makes use of the new CUDA software/hardware architecture developed recently by NVIDIA. Key–Words: Quantum computing simulation, Graphics Processing Unit (GPU), parallel processing 1
We present the first application of field programmable gate arrays (FPGAs) as new, customizable hardware architectures that can be harnessed for the fast and energy-efficient calculation of quantum dynamics simulations of large chemical/material systems. Instead of tailoring the software to fixed hardware (which is the typical case for writing quantum chemistry code for CPUs/GPUs), FPGAs allow us to directly customize the underlying hardware - even at the level of specific electrical signals in the circuit - to give a truly optimized computational performance for complex quantum dynamics calculations. By offloading the most intensive and repetitive calculations onto an FPGA, we show that the computational performance of our hardware implementation for real-time electron dynamics calculations can even exceed that of optimized commercial mathematical libraries running on high-performance GPUs. In addition to this impressive computational speedup, we show that FPGAs are immensely energy-efficient and consume 4 times less energy than modern GPU or CPU architectures. These energy savings are a practical and important metric for supercomputing centers (several of which exceed over $1 million in power costs alone), as exascale computing capabilities become more widespread and commonplace. Taken together, the implementation techniques and performance metrics of our study demonstrate that FPGAs could play a promising role in upcoming quantum chemistry and materials science applications, particularly for the acceleration and energy-efficient execution of quantum dynamics calculations.