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 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.
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.
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.
A quantum computing simulation provides the opportunity to explore the behaviors of quantum circuits, study the properties of quantum gates, and develop quantum computing algorithms. Simulating quantum circuits requires geometric time and space complexities, impacting the size of the quantum circuit that can be simulated as well as the respective time required to simulate a particular circuit. Applying the parallelism inherent in the simulation and crafting custom architectures, larger quantum circuits can be simulated. A scalable accelerator architecture is proposed to provide a high performance, highly parallel, accelerator. Among the challenges of creating a scalable architecture is managing parallelism, efficiently routing quantum state components for gate evaluation, and measurement. An example is demonstrated on an Intel Agilex field programmable gate array (FPGA).
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.
ABSTRACT In recent years, quantum emulation has attracted increasing attention as a practical approach for developing and evaluating quantum algorithms. However, existing solutions often face challenges in achieving broad applicability, high performance, and efficient utilization of hardware resources and memory. To address these limitations, this paper presents the high‐performance quantum emulation accelerator (HPQEA), a hardware architecture based on the state‐vector emulation approach. The proposed design incorporates four key techniques: a strict data storage strategy, a superior flexible processing element network, an optimized and parallelized CX swapper, and a high‐bandwidth memory‐based bulk data transfer engine. Implementation and evaluation on the Alveo U280 FPGA platform demonstrate that HPQEA can emulate quantum circuits efficiently while maintaining high fidelity and low mean square error. Experimental results show that for QFT benchmarks, HPQEA achieves up to 21,093 faster execution than the NVIDIA A100 GPU for circuits with fewer than 20 qubits. Moreover, the comparison with FPGA‐based related works shows promising results with significant speedup in terms of normalize gate speed from 2.77 to 1448.37. In addition, the architecture demonstrates strong energy efficiency, reducing power consumption by up to 15.58 compared to the Intel i9‐10940X CPU and 80.83 compared to the GTX 1080 GPU. These results highlight the scalability, efficiency, and practicality of HPQEA as a hardware platform for accelerating quantum algorithm emulation.
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.