A Scheduling Method for Asynchronous VLSI System Design

Introduction With the revival of interest in asynchronous systems there is a need for methods and tools for the high-level synthesis tailored for them. Although there are various methodologies already published that deal with synchronous design, there is not much work developed for the scheduling of asynchronous VLSI systems. In particular, we are interested in a methodology to schedule asynchronous pipelines. In this note we propose a method for scheduling asynchronous VLSI systems, and then show how this method, together with existing synchronous methodologies can be used to synthesize asynchronous pipelines. 2 Asynchronous scheduling Given a data flow graph (DFG), a set of constraints (performance/area), and a set of libraries of functional modules, the task of scheduling an asynchronous system is to decide how to place the functional modules so that they satisfy the constraints, and at the same time trying to minimize the necessary resources. In the meth

Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles

We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the ab-initio VASP program. Benchmarking was carried out on Au20 nanoclusters against ab-initio molecular dynamics simulations and show we can achieve similar accuracy with the machine learned potential at far reduced cost using LAMMPS. We have been able to reproduce structures and heat capacities of several isomeric forms. Comparison of our workflow with similar ML-IP studies is discussed and has identified areas for future improvement

Efficient development of high performance data analytics in Python

Our society is generating an increasing amount of data at an unprecedented scale, variety, and speed. This also applies to numerous research areas, such as genomics, high energy physics, and astronomy, for which large-scale data processing has become crucial. However, there is still a gap between the traditional scientific computing ecosystem and big data analytics tools and frameworks. On the one hand, high performance computing (HPC) programming models lack productivity, and do not provide means for processing large amounts of data in a simple manner. On the other hand, existing big data processing tools have performance issues in HPC environments, and are not general-purpose. In this paper, we propose and evaluate PyCOMPSs, a task-based programming model for Python, as an excellent solution for distributed big data processing in HPC infrastructures. Among other useful features, PyCOMPSs offers a highly productive general-purpose programming model, is infrastructure-agnostic, and provides transparent data management with support for distributed storage systems. We show how two machine learning algorithms (Cascade SVM and K-means) can be developed with PyCOMPSs, and evaluate PyCOMPSs’ productivity based on these algorithms. Additionally, we evaluate PyCOMPSs performance on an HPC cluster using up to 1,536 cores and 320 million input vectors. Our results show that PyCOMPSs achieves similar performance and scalability to MPI in HPC infrastructures, while providing a much more productive interface that allows the easy development of data analytics algorithms.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND2016-754433. This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya, Spain (contract 2014-SGR-1051). The research leading to these results has also received funding from the collaboration between Fujitsu and BSC (Script Language Platform).Peer Reviewe