arXiv subjects

Kaveh Haghighi Mood

Publications and source records attributed to Kaveh Haghighi Mood.

6 recordsLinked to original sources

arXiv2026

Performance Evaluation of Fast Fourier Transforms on Emerging RISC-V Hardware with Vector Extension Support

This manuscript presents a performance evaluation of Fast Fourier Transform (FFT) implementations on emerging processors supporting the RISC-V Vector Extension (RVV 1.0). By introducing juFFTe, a light-weight high-performance library for discrete Fourier transforms, it is demonstrated how effective vectorization of performance-critical FFT kernels can be achieved on RVV-enabled hardware. Comprehensive benchmarks on three RVV 1.0-ready processors, the SiFive X280, the X100 core of the SpacemiT K3 and the C920v2 core of the Sophon SG2044, reveal substantial performance improvements of juFFTe (https://github.com/FZJ-JSC/juFFTe) over the widely used FFTW3 library. Although RVV-enabled platforms show promising results at this stage of development, a comparison with AMD's Zen 5 architecture indicates that RISC-V needs further maturing to reach the performance of established micro-architectures.

cs.MS↗

arXiv2026

Great Expectations: Benchmarking the Real-World Performance of RVV 1.0 in HPC

Following the ratification of the RISC-V Vector Extension (RVV 1.0), new commercially available silicon has been adopting the extension. This paper revisits the question of RISC-V viability for High-Performance-Computing (HPC) by benchmarking the latest RVV 1.0-capable hardware (SiFive X280 (Tenstorrent Blackhole), SpacemiT X60 (K1) and X100/A100 (K3), and T-Head C920v2 (Sophon SG2044)). We assess these platforms using standard HPC benchmarks (BLAS, FFTW, HPL, HPCG) and synthetic workloads (STREAM, FMA throughput) and compare them to a state-of-the-art HPC ARM64 chip (NVIDIA Grace). Our findings show that while RVV 1.0 delivers significant performance improvements over scalar execution, hardware-specific implementation challenges remain. We detail these performance characteristics and discuss the remaining hurdles for RISC-V, including RVV, to become a mainstay in the HPC landscape.

cs.DC↗

arXiv2024

Application-Driven Exascale: The JUPITER Benchmark Suite

Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to guarantee high usability and widespread adoption of a new system. Given the significant investments in leadership-class supercomputers of the exascale era, this is even more important and necessitates alignment with a vision of Open Science and reproducibility. In this work, we present the JUPITER Benchmark Suite, which incorporates 16 applications from various domains. It was designed for and used in the procurement of JUPITER, the first European exascale supercomputer. We identify requirements and challenges and outline the project and software infrastructure setup. We provide descriptions and scalability studies of selected applications and a set of key takeaways. The JUPITER Benchmark Suite is released as open source software with this work at https://github.com/FZJ-JSC/jubench.

cs.DC↗

arXiv2021

A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations

Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability of detailed anatomical data on brain connectivity. Large-scale models that study interactions between multiple brain areas with intricate connectivity and investigate phenomena on long time scales such as system-level learning require progress in simulation speed. The corresponding development of state-of-the-art simulation engines relies on information provided by benchmark simulations which assess the time-to-solution for scientifically relevant, complementary network models using various combinations of hardware and software revisions. However, maintaining comparability of benchmark results is difficult due to a lack of standardized specifications for measuring the scaling performance of simulators on high-performance computing (HPC) systems. Motivated by the challenging complexity of benchmarking, we define a generic workflow that decomposes the endeavor into unique segments consisting of separate modules. As a reference implementation for the conceptual workflow, we develop beNNch: an open-source software framework for the configuration, execution, and analysis of benchmarks for neuronal network simulations. The framework records benchmarking data and metadata in a unified way to foster reproducibility. For illustration, we measure the performance of various versions of the NEST simulator across network models with different levels of complexity on a contemporary HPC system, demonstrating how performance bottlenecks can be identified, ultimately guiding the development toward more efficient simulation technology.

q-bio.NC↗

arXiv2019

Full Wave Function Optimization with Quantum Monte Carlo -- A study of the Dissociation Energies of ZnO, FeO, FeH, and CrS

The dissociation energies of four transition metal dimers are determined using diffusion Monte Carlo. The Jastrow, CI, and molecular orbital parameters of the wave function are both partially and fully optimized with respect to the variational energy. The pivotal role is thereby ascribable to the optimization of the molecular orbital parameters of a complete active space wave function in the presence of a Jastrow correlation function. Excellent results are obtained for ZnO, FeO, FeH, and CrS. In addition, potential energy curves are computed for the first three compounds at multi-reference diffusion Monte Carlo (MR-DMC) level, from which spectroscopic constants such as the equilibrium bond distance, the harmonic frequency, and the anharmonicity are extracted. All of those quantities agree well with the experiment. Furthermore, it is shown for CrS that a restricted active space calculation can yield improved initial orbitals by including single and double excitations from the original active space into a set of virtual orbitals. We demonstrated in this study that the fixed-node error in DMC can be systematically reduced for multi-reference systems by orbital optimization in compact active spaces. While DMC calculations with a large number of determinants are possible and very accurate, our results demonstrate that compact wave functions may be sufficient in order to obtain accurate nodal surfaces, which determine the accuracy of DMC, even in the case of transition metal compounds.

physics.chem-ph↗

arXiv2017

Full wave function optimization with quantum Monte Carlo and its effect on the dissociation energy of FeS

Diffusion quantum Monte Carlo calculations with partial and full optimization of the guide function are carried out for the dissociation of the FeS molecule. For the first time, quantum Monte Carlo orbital optimization for transition metal compounds is performed. It is demonstrated that energy optimization of the orbitals of a complete active space wave function in the presence of a Jastrow correlation function is required to obtain agreement with the experimental dissociation energy. Furthermore, it is shown that orbital optimization leads to a $^5Δ$ ground state, in agreement with experiments, but in disagreement with other high-level ab initio wave function calculations which all predict a $^5Σ^+$ ground state. The role of the Jastrow factor in DMC calculations with pseudo potentials is investigated. The results suggest that a large Jastrow factor may improve the DMC accuracy substantially at small additional cost.

physics.chem-ph↗