Serial Execution Research Articles

A Parallel Monte Carlo Algorithm for the Life Cycle Asset Allocation Problem

Life cycle asset allocation is a crucial aspect of financial planning, especially for pension funds. Traditional methods often face challenges in computational efficiency and applicability to different market conditions. This study aimed to innovatively transplant an algorithm from reinforcement learning that enhances the efficiency and accuracy of life cycle asset allocation. We synergized tabular methods with Monte Carlo simulations to solve the pension problem. This algorithm was designed to correspond states in reinforcement learning to key variables in the pension model: wealth, labor income, consumption level, and proportion of risky assets. Additionally, we used cleaned and modeled survey data from Chinese consumers to validate the model’s optimal decision-making in the Chinese market. Furthermore, we optimized the algorithm using parallel computing to significantly reduce computation time. The proposed algorithm demonstrated superior efficiency compared to the traditional value iteration method. Serial execution of our algorithm took 29.88 min, while parallel execution reduced this to 1.42 min, compared to the 41.15 min required by the value iteration method. These innovations suggest significant potential for improving pension fund management strategies, particularly in the context of the Chinese market.

From GPU to CPU (and Beyond): Extending Hardware Support in GPUSPH Through a SYCL‐Inspired Interface

ABSTRACTWhile most software is originally designed for serial or parallel execution on CPU, and porting to GPU comes later in its development, GPUSPH was designed from the ground up to run on GPUs using CUDA. Making it accessible to a wider audience by introducing support for other computational hardware, and in particular CPUs, poses challenges that are complementary to the ones normally faced when porting CPU code to GPU. We present the approach we have adopted to support CPUs as computational devices in GPUSPH with minimal code changes and low developer effort. Detailed benchmarks illustrating the performance of the implementation and its scalability across multiple cores in both single‐socket and NUMA configurations show good strong and weak scaling.

Linear Sources and Anisotropic Scattering in the Random Ray Method

An enhanced implementation of the random ray method (TRRM), a stochastic adaptation of the method of characteristics, is presented. This implementation generalizes from a traditional flat source (FS) approximation to a linear source (LS) approximation, and from the standard isotropic scattering approximation to an anisotropic approximation, up to P3. On the two-dimensional C5G7 benchmark, LS alone enables a 1.4× and 1.7× enhancement in run time for parallel and serial executions, respectively, without compromising accuracy. Further testing on the three-dimensional C5G7 Rodded B benchmark demonstrates that LS enables significant axial coarsening and reduction in ray populations. This results in a 5.3× speedup in run time while still offering comparable accuracy. On the Babcock and Wilcox 1484 Core II benchmark, incorporating anisotropic scattering in TRRM up to P3 with a FS decreased keff errors (~110 pcm) and maximum and average pin power errors. LS with anisotropic scattering (LSPN), showed similarly improved keff errors while benefiting from a coarser mesh. The linear isotropic flat anisotropic (LIFA) method, relative to LSPN, offers further run time and memory savings of 4.1× and 1.6×, respectively, for P3, while maintaining comparable accuracy to anisotropic FS on more refined meshes.

Make optical transmission quality prediction more effective: A parallel computing view

In optical networks, ensuring high quality of transmission (QoT) is essential to prevent degradation of optical signals, especially when the signal strength falls below a specified threshold. While machine learning (ML) is widely used for QoT prediction, predicting QoT accurately for large-scale optical links presents challenges. Traditional serial methods often result in high latency and decreased processing efficiency of optical channels. To solve this problem, this paper proposes a Dask-based P-FEDformer approach. Initially, a FEDformer-based predictor is constructed, and then QoT prediction for multiple channels is realized under the Dask parallel architecture. To enhance model prediction accuracy, wavelet decomposition technique is employed. Simulation results demonstrate the method’s effectiveness in handling large amount of data with a 60% improvement in time efficiency compared to serial execution, while maintaining accurate QoT prediction.

SMATool: Strength of materials analysis toolkit

The study of the strength of materials is a cornerstone in material science and engineering, playing a critical role in shaping the progress and application of materials in diverse industrial sectors. The strength of a material is meticulously examined to understand the behavior of the material under different stress conditions and environments, thereby guiding material selection and structural design. Herein, we introduce the SMATool, a computational toolkit for the efficient calculation and analysis of material strength at both zero and finite temperatures for 3D, 2D, 1D, and tubular 2D-based nanostructures and nanotubes, as well as 1D nanoribbons. The toolkit is capable of calculating tensile, shear, ultimate, yield, and indentation (Vickers' hardness) strengths in various dimensions, as well as the energy storage capacity. We conducted several calculations both at zero and finite temperatures to validate the accuracy and reliability of the developed software. The results show that the SMATool package provides accurate predictions that align with existing data on material strength. SMATool integrates seamlessly with widely used electronic structure codes like VASP and Quantum Espresso, providing a user-friendly interface catering to academic researchers and industry professionals. SMATool is useful for either the exploration of the strength of materials or high-throughput new material design. SMATool is open-source and available on GitHub at SMATool@github and at Zenodo at 10.5281/zenodo.10780514. Program summaryProgram Title: SMAToolCPC Library link to program files:https://doi.org/10.17632/fnkzfsg7t7.1Licensing provisions: GNU General Public License, version 3Programming language: Python 3Nature of problem: Developing a comprehensive framework to accurately determine the strength of materials is crucial in various engineering and scientific disciplines. This involves developing a robust and efficient computational toolkit to predict the strength of a material under different loading conditions, including tension, compression, shear, and indentation.Solution method:SMATool effectively addresses the challenge of accurately simulating the mechanical properties of materials by leveraging advances in ab initio methods, including density functional theory and molecular dynamics within the atomic simulation environment. The SMATool toolkit can efficiently compute various material strengths, such as tensile, shear, ultimate, yield, and indentation (Vickers' hardness) strengths as well as the corresponding energy storage capacity of the material. It is adept at handling a variety of material dimensions, ranging from 1D nanotubes and nanoribbons to 2D and 3D structures, at both zero and finite temperatures. SMATool currently integrates VASP and Quantum Espresso as the stress calculator and is easily adaptable to use other electronic structure codes.Additional comments including restrictions and unusual features:SMATool offers serial and parallel execution. Depending on the system size, the nature of the calculations (zero or finite temperature), and the computing architecture, the computing time can vary from ∼5 minutes to as high as 18 hours.

ElasTool v3.0: Efficient computational and visualization toolkit for elastic and mechanical properties of materials

Efficient computation and visualization of elastic and mechanical properties are crucial in the selection of materials and the design of new materials. The ElasTool v3.0 toolkit marks a significant advancement in the computational analysis and visualization of elastic and mechanical properties of materials, essential in material selection and design. This enhanced version extends beyond standard calculations like elastic tensor, Young's modulus, bulk modulus, and Poisson's ratio. It introduces capabilities for computing minimum thermal conductivity, linear compressibility, rendering the Christoffel equation, and elastic energy density. Notably, it integrates advanced visualization tools, including compatibility with Plotly and Elate web platforms for interactive web-based property exploration. A key feature of ElasTool v3.0 is the implementation of second-order elastic constants (SOECs) for tubular 2D-based nanostructures and nanotubes. Leveraging high-efficiency strain-matrix sets (OHESS), the toolkit now facilitates efficient computation of elastic constants and mechanical properties at both zero and finite temperatures for 1D, 2D, and 3D dimensions. ElasTool is openly accessible on GitHub: https://github.com/elastool. Program summaryProgram Title: ElasTool v3.0CPC Library link to program files:https://doi.org/10.17632/9hpgmspfmk.1Licensing provisions: GNU General Public License, version 3Programming language: Python 3Nature of problem: A computational toolkit that can efficiently compute the elastic and mechanical properties of materials using ab initio methods and provide advanced visualization capabilities is crucial to accurate material analysis.Solution method:ElasTool addresses this need by incorporating high-efficiency strain-matrix sets that require only small strain sets with ab initio methods such as density functional theory and molecular dynamics. This package is versatile and adept at calculating elastic constants for both 2D and 3D structures, including tubular 2D-based nanostructures and nanotubes and 1D nanoribbons at zero and finite temperatures. Beyond basic elastic moduli, ElasTool now boasts advanced visualization capabilities and computes additional critical properties such as minimum thermal conductivity, elastic energy density, and linear compressibility. This enhancement transforms ElasTool into a comprehensive tool for accelerated material design and modeling, and advanced visualization, offering both efficiency and depth in material property analysis.Additional comments including restrictions and unusual features:ElasTool is currently integrated with the Vienna Ab initio Simulation Package (VASP). Adapting to other electronic structures is straightforward.ElasTool offers serial and parallel execution. Depending on the system size, the number of strains, the nature of the calculations (zero or finite temperature), and the computing architecture, the computing time can vary from a minute to as long as 15 hours.

Unraveling the hierarchical structure of posture and muscle activity changes during mating of Caenorhabditis elegans.

One goal of neurobiology is to explain how decision-making in neuromuscular circuits produces behaviors. However, two obstacles complicate such efforts: individual behavioral variability and the challenge of simultaneously assessing multiple neuronal activities during behavior. Here, we circumvent these obstacles by analyzing whole animal behavior from a library of Caenorhabditis elegans male mating recordings. The copulating males express the GCaMP calcium sensor in the muscles, allowing simultaneous recording of posture and muscle activities. Our library contains wild type and males with selective neuronal desensitization in serotonergic neurons, which include male-specific posterior cord motor/interneurons and sensory ray neurons that modulate mating behavior. Incorporating deep learning-enabled computer vision, we developed a software to automatically quantify posture and muscle activities. By modeling, the posture and muscle activity data are classified into stereotyped modules, with the behaviors represented by serial executions and transitions among the modules. Detailed analysis of the modules reveals previously unidentified subtypes of the male's copulatory spicule prodding behavior. We find that wild-type and serotonergic neurons-suppressed males had different usage preferences for those module subtypes, highlighting the requirement of serotonergic neurons in the coordinated function of some muscles. In the structure of the behavior, bi-module repeats coincide with most of the previously described copulation steps, suggesting a recursive "repeat until success/give up" program is used for each step during mating. On the other hand, the transition orders of the bi-module repeats reveal the sub-behavioral hierarchy males employ to locate and inseminate hermaphrodites.

Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization

The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. However, the current approach faces a significant hurdle due to the computational intensity of the process, particularly when employing complex optimization techniques like swarm intelligence optimization alongside a costly ATS repair operator. While the current approach yields impressive Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics for the generated summary, it struggles with inefficiencies, mainly attributed to the substantial optimization time consumed by the ATS repair operator scheme. In order to address this, a novel solution called Decomposition-based Multi-Objective Differential Evolution (MODE/D) is proposed. It is built upon the foundation of Differential Evolution for Multi-Objective Optimization (DEMO) and the weighted sum method (WS), coupled with an innovative ATS repair operator scheme. Through experimentation on Document Understanding Conferences (DUC) datasets, the novel approach of MODE/D – WS is validated by evaluating the results using ROUGE metrics. The outcomes are twofold: a remarkable reduction in serial execution time and a noteworthy enhancement over existing techniques in the scholarly domain, as evidenced by improved ROUGE-1, ROUGE-2, and ROUGE-L scores.

Multi-objective joint optimization for concurrent execution of design-construction tasks in design-build mode

PERT (Program Evaluation and Review Technique) is suitable for evaluating schedule optimization of DBB (Design-Bid-Build) projects based on serial execution of design and construction. However, it is not suitable for evaluating the multi-objective joint optimization of DB (Design-Build) projects based on concurrent execution of design and construction. Therefore, focusing on the unique construction method and construction objective under DB mode, this study adopts the multi-parameter grey GERT (Graphical Evaluation and Review Technique) based on z-tag to construct a multi-objective joint optimization model of concurrent execution of design-construction tasks. The research methods adopted are as follows: Firstly, based on the modeling idea of PERT, a multi-parameter grey GERT was constructed, which was composed of sub-GERTs representing the execution flow of design-construction tasks. Second, based on discussing the functional relationship between time, cost and quality in normal execution and rework execution, a method of constructing a moment generating function for concurrent execution time, cost and quality to characterize the uncertainty of input and output parameters is proposed. Thirdly, on the basis of analyzing the solving methods of each parameter of the multi-parameter GERT, the solving methods for starting execution time, information transmission duration, and total execution duration of the critical time nodes are given. Finally, according to the requirements of DB projects, the multi-objective joint optimization models based on progress oriented and quality oriented are constructed respectively, and the gamultiobj algorithm is used to solve the models. The proposed approach provides a scientific quantitative analysis method for evaluating the progress, cost and quality objectives of the concurrent execution of design-construction tasks, and also lays theoretical guidance for further efficiency popularization of DB mode.

TurboGNN: Improving the End-to-End Performance for Sampling-Based GNN Training on GPUs

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Graph Neural Networks</i> (GNN) have evolved as powerful models for graph representation learning. Sampling-based training methods have been introduced to train large graphs without compromising accuracy. However, it is challenging for the existing GNN systems to effectively utilize multi-core accelerators, especially GPUs, due to a large number of atomic operations and unbalanced workload originating from the serial execution of multiple GNN processing stages. In this paper, we propose a combination of optimization techniques to accelerate the end-to-end performance of the sampling-based GNN training process. Specifically, we propose an adaptive share memory-based sampling technique and a degree-guided thread block scheduling strategy to optimize the graph sampling. Further, based on the observations of resource demand in different training stages, we propose an asynchronous pipeline-based scheduling method, which accelerates the GNN training by decoupling different training stages into a pipeline and therefore improves the GPU resource utilization significantly. The experimental results show that compared with the existing work, the proposed methods can achieve up to 5.6X performance speedup in the end-to-end performance.

OceanBase Paetica: A Hybrid Shared-Nothing/Shared-Everything Database for Supporting Single Machine and Distributed Cluster

In the ongoing evolution of the OceanBase database system, it is essential to enhance its adaptability to small-scale enterprises. The OceanBase database system has demonstrated its stability and effectiveness within the Ant Group and other commercial organizations, besides through the TPC-C and TPC-H tests. In this paper, we have designed a stand-alone and distributed integrated architecture named Paetica to address the overhead caused by the distributed components in the stand-alone mode, with respect to the OceanBase system. Paetica enables adaptive configuration of the database that allows OceanBase to support both serial and parallel executions in stand-alone and distributed scenarios, thus providing efficiency and economy. This design has been implemented in version 4.0 of the OceanBase system, and the experiments show that Paetica exhibits notable scalability and outperforms alternative stand-alone or distributed databases. Furthermore, it enables the transition of OceanBase from primarily serving large enterprises to truly catering to small and medium enterprises, by employing a single OceanBase database for the successive stages of enterprise or business development, without the requirement for migration. Our experiments confirm that Paetica has achieved linear scalability with the increasing CPU core number within the stand-alone mode. It also outperforms MySQL and Greenplum in the Sysbench and TPC-H evaluations.

Multi-axis Current Loop Control Based on FPGA

In this paper, according to the three-loop control structure, FPGA is used as the control chip of the current loop, and the parallel control of the multi-axis motor is realized by hardware nation. The current loop code is written in Verilog HDL language, and the simulation results of each control module are given by the SignalTap simulation tool. The main contents include a parallel sampling of the multichannel encoder, parallel sampling of multichannel current, vector coordinate transformation control (CLARK module, PARK module, IPARK module), PI controller module, etc. The results show that the rich logic resources and powerful parallel processing ability of FPGA enable a single FPGA to control multiple motors at the same time. Compared with the traditional serial execution of the program, this method greatly reduces the whole calculation time of the servo system.

DYNAMICS OF THE EEG SENSORIMOTOR RHYTHM DURING MENTAL REPETITION OF THE OBSERVED MOVEMENT

Mental simulation of one’s own movement, or imagery of movement, as well as observation of other people’s movements are used in neurorehabilitation as methods of stimulation of sensorimotor parts of the brain. The present work tests a new way of representation - mental simulation of movement, synchronous with the movement observed from the first person on a video screen. The objectives of the study were to compare the reactivity of sensorimotor EEG rhythms during voluntary movement representation and representation following a video stimulus, and to identify the relationship between the phases of movement in the video and the dynamics of EEG patterns. The study involved 30 healthy volunteers in whom a 69-channel encephalogram was recorded during their performance and presentation of right thumb movements in two modes: arbitrarily (without an external reference) and synchronously imitating movement on a video clip. During EEG analysis, individual spatial-frequency components with the highest EEG mu-rhythm reactivity (8–14 Hz) were identified in the subjects, followed by quantitative assessment of desynchronization under the studied conditions based on analysis of probability density distributions of mu-rhythm power. A generalized additive model describing the function of responses to single events in the observed movements and their summation during serial execution or presentation of the movements was applied to assess the relationship between the dynamics of mu-rhythm desynchronization and video events. It was shown that the mental kinesthetic simulation of the observed movement did not result in increased desynchronization of sensorimotor rhythms compared to the voluntary representation of the same movement. It was found for the first time that there are perturbations in the temporal course of desynchronization of the mu-rhythm that depend on the phase and speed of the observed movement both during its synchronous muscle repetition and during mental synchronous imitation. The results obtained can be used to optimize movement parameters in individual systems of ideomotor training with EEG control to achieve the greatest sensorimotor activation.

CUDA-BASED PARALLELIZATION OF GRADIENT BOOSTING AND BAGGING ALGORITHM FOR DIAGNOSING DIABETES

Data, its volume, structure, and form of presentation are among the most significant problems in working in the medical field. The probability of error is very high without innovative high-tech data analysis tools. It is easy to miss an important factor that is critical but lost among other, less important information. This work aims to study the proposed parallel gradient boosting algorithm in combination with the Bagging algorithm in the classification of diabetes to achieve greater stability and higher accuracy, reduce computational complexity and improve performance in medicine. The methods of parallelization of the Gradient Boosting algorithm in combination with the Bagging algorithm are investigated in the paper. Performance scores were obtained: approximately 7 using ThreadPoolExecutor and an eight-core computer system and 9.5 based on CUDA technology. Performance indicators that go to the unit are calculated. This, in turn, confirms the effectiveness of the proposed parallel algorithm. Another significant result of the study is improving algorithm accuracy by increasing the number of algorithms in the composition. The problem of diagnosing a patient's diabetes based on specific measurements included in the data set is considered. Detailed analysis and pre-processing of the selected dataset were performed. The parallelization of the proposed algorithm is implemented using the multi-core architecture of modern computers and CUDA technology. The process of learning models and training samples was parallelized. The theoretical estimation of the computational complexity of the offered parallel algorithm is given. A comparison of serial and parallel algorithm execution time using ThreadPoolExecutor when varying the number of threads and algorithms in the composition is presented. And also, the comparative analysis of time expenses at consecutive and parallel execution based on CPU and GPU is carried out.

Virtualizing Existing Fluidic Programs

Fluidic automation, the practice of programmatically manipulating small fluids to execute laboratory protocols, has led to vastly increased productivity for biologists and chemists. Most fluidic programs, commonly referred to as protocols, are written using APIs that couple the protocol to specific hardware by referring to the physical locations on the device. This coupling makes isolation impossible, preventing portability, concurrent execution, and composition of protocols on the same device. We propose a system for virtualizing existing fluidic protocols on top of a single runtime system without modification. Our system presents an isolated view of the device to each running protocol, allowing it to assume it has sole access to hardware. We provide a proof-of-concept implementation that can concurrently execute and compose protocols written using the popular Opentrons Python API. Concurrent execution achieves near-linear speedup over serial execution, since protocols spend much of their time waiting.

Sgap: towards efficient sparse tensor algebra compilation for GPU

Sparse compiler is a promising solution for sparse tensor algebra optimization. In compiler implementation, reduction in sparse-dense hybrid algebra plays a key role in performance. Though GPU provides various reduction semantics that can better utilize the parallel computing and memory bandwidth capacity, the central question is: how to elevate the flexible reduction semantics to sparse compilation theory that assumes serial execution. Specifically, we have to tackle two main challenges: (1) there are wasted parallelism by adopting static synchronization granularity (2) static reduction strategy limits optimization space exploration. We propose Sgap: s egment g roup and a tomic p arallelism to solve these problems. Atomic parallelism captures the flexible reduction semantics to systematically analyze the optimization space of sparse-dense hybrid algebra on GPU. It is a new optimization technique beyond current compiler-based and open-source runtime libraries. Segment group elevates the flexible reduction semantics to suitable levels of abstraction in the sparse compilation theory. It adopts changeable group size and user-defined reduction strategy to solve challenge (1) and (2), respectively. Finally, we use GPU sparse matrix-matrix multiplication (SpMM) on the TACO compiler as a use case to demonstrate the effectiveness of segment group in reduction semantics elevation. We achieve up to $$1.2\,\times$$ speedup over the original TACO’s SpMM kernels. We also apply new optimization techniques found by atomic parallelism to an open-source state-of-the-art SpMM library dgSPARSE. We achieve $$1.6 \, \times \sim 2.3 \, \times$$ speedup on the algorithm tuned with atomic parallelism.

Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems

VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of [ 32 ], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.

A Preliminary Study of Large Scale Pulsar Candidate Sifting Based on Parallel Hybrid Clustering

Pulsar candidate sifting is an essential part of pulsar analysis pipelines for discovering new pulsars. To solve the problem of data mining of a large number of pulsar data using a Five-hundred-meter Aperture Spherical radio Telescope (FAST), a parallel pulsar candidate sifting algorithm based on semi-supervised clustering is proposed, which adopts a hybrid clustering scheme based on density hierarchy and the partition method, combined with a Spark-based parallel model and a sliding window-based partition strategy. Experiments on the two datasets, HTRU (The High Time-Resolution Universe Survey) 2 and AOD-FAST (Actual Observation Data from FAST), show that the algorithm can excellently identify the pulsars with high performance: On HTRU2, the Precision and Recall rates are 0.946 and 0.905, and those on AOD-FAST are 0.787 and 0.994, respectively; the running time on both datasets is also significantly reduced compared with its serial execution mode. It can be concluded that the proposed algorithm provides a feasible idea for astronomical data mining of FAST observation.

GPU and CPU-Based Parallel FDTD Methods for Frequency-Dependent Transmission Line Models

Finite-difference time-domain (FDTD) is a popular method utilized for solving frequency-dependent transmission line structures. It is also conveniently applicable to nonuniform wires. The FDTD algorithm discretizes the transmission line problem into a finite number of space-segments and solve for the voltage and current of each segment at every time-step. Therefore, they inherently involve more computations per timestep than conventional terminal based models. In this letter, parallel implementations of a modified FDTD algorithm for a frequency-dependent transmission line problem using multicore CPU and GPU architectures are proposed in order to increase its computational efficiency. Accuracy and performance of the parallel FDTD methods are discussed with examples. The results indicate that a speedup of a few folds compared to serial execution is achieved by the parallel implementation using multicore CPU architecture whereas a massive speedup is achieved by using GPU. The proposed model is also suitable for modelling transmission lines in massively parallel electromagnetic transient (EMT) simulation methods.

Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms

We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing quadratically in the target error and we extend this to a quadratic error scaling advantage in market risk computation. We show that employing quantum gradient estimation algorithms can deliver a further quadratic advantage in the number of the associated market sensitivities, usually called greeks. By numerically simulating the quantum gradient estimation algorithms on financial derivatives of practical interest, we demonstrate that not only can we successfully estimate the greeks in the examples studied, but that the resource requirements can be significantly lower in practice than what is expected by theoretical complexity bounds. This additional advantage in the computation of financial market risk lowers the estimated logical clock rate required for financial quantum advantage from Chakrabarti et al. [Quantum 5, 463 (2021)] by a factor of ~7, from 50MHz to 7MHz, even for a modest number of greeks by industry standards (four). Moreover, we show that if we have access to enough resources, the quantum algorithm can be parallelized across 60 QPUs, in which case the logical clock rate of each device required to achieve the same overall runtime as the serial execution would be ~100kHz. Throughout this work, we summarize and compare several different combinations of quantum and classical approaches that could be used for computing the market risk of financial derivatives.