141 research outputs found
Automatic synthesis of high performance translation operators and execution plans for the fast multipole method
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01The student, Isuru Fernando, accepted the attached license on 2023-07-10 at 19:11.The student, Isuru Fernando, submitted this Dissertation for approval on 2023-07-10 at 19:15.This Dissertation was approved for publication on 2023-07-11 at 14:15.DSpace SAF Submission Ingestion Package generated from Vireo submission #19613 on 2023-12-04 at 17:18:27The Fast Multipole Method (FMM) is the leading approach for attaining linear complexity in the evaluation of long-range (e.g. Coulomb) many-body interactions. The intricacies of implementing a high performant FMM for different potentials are a major barrier to the widespread use of the FMM. In the application of the Fast Multipole Method to the computation of potentials for elliptic PDEs and systems thereof, I present methods that, given various small amounts of user-supplied problem knowledge automatically exploit this knowledge to optimize evaluating the potentials. The first part of the thesis is devoted to the automatic synthesis of translation operators (e.g. multipole-to-local, point-to-multipole, etc.) for arbitrary kernels. I describe the asymptotic cost of variants of our algorithm available given certain pieces of information, as well as the methods by which they are attained. I present theoretical cost bounds as well as numerical evidence that our algorithms attain them. The second part of the thesis describes my work in extending the first to a system of PDEs. I introduce algorithms to automatically synthesize execution plans for expressions of potential operators involving multiple inputs and outputs, multiple different kernels, as well as source and target derivatives. Given a symbolic description of such an operator, the system outputs a sequence of operations that realizes cost savings through an algebraic procedure based on syzygies. Finally, I describe the work of using the above algorithms to generate fast code for graphical processing units (GPUs) that achieve near peak performance and explaining the performance characteristics of the different operations in the FMM using a performance model
Community creation by means of a social media paradigm
No full textPurposeThe purpose of this paper is to present a case study from which a framework for the purposeful building of knowledge communities by means of social media is formulated.Design/methodology/approachThe approach first takes the form of a literature review. Based on a review of literature as well as on various data sets and surveys within the case study organisation, a social media tool is developed and implemented as a platform to build a knowledge community.FindingsThat there is a need for more information by means of practical experimentation in the current literature. In addition to this, eight steering points for constructing a framework of best practice in the purposeful creation of knowledge communities are submitted.Research limitations/implicationsThe above‐mentioned points are still undergoing field evaluation and further analytics is been undertaken.Originality/valueThe paper provides detail into a social media campaign. Further corporations will be able to build on the findings to construct a framework for the purposeful creation of communities facilitated by social media technologies.</jats:sec
scipy/scipy: SciPy 1.7.3
No full textSciPy 1.7.3 Release Notes
SciPy 1.7.3 is a bug-fix release that provides binary wheels
for MacOS arm64 with Python 3.8, 3.9, and 3.10. The MacOS arm64 wheels
are only available for MacOS version 12.0 and greater, as explained
in Issue 14688.
Authors
Anirudh Dagar
Ralf Gommers
Tyler Reddy
Pamphile Roy
Olivier Grisel
Isuru Fernando
A total of 6 people contributed to this release.
People with a "+" by their names contributed a patch for the first time.
This list of names is automatically generated, and may not be fully complete
Automatic Synthesis of Low-Complexity Translation Operators for the Fast Multipole Method
No full textWe demonstrate a new, hybrid symbolic-numerical method for the automatic
synthesis of all families of translation operators required for the execution
of the Fast Multipole Method (FMM). Our method is applicable in any
dimensionality and to any translation-invariant kernel. The Fast Multipole
Method, of course, is the leading approach for attaining linear complexity in
the evaluation of long-range (e.g. Coulomb) many-body interactions. Low
complexity in translation operators for the Fast Multipole Method (FMM) is
usually achieved by algorithms specialized for a potential obeying a specific
partial differential equation (PDE). Absent a PDE or specialized algorithms,
Taylor series based FMMs or kernel-independent FMM have been used, at
asymptotically higher expense.
When symbolically provided with a constant-coefficient elliptic PDE obeyed by
the potential, our algorithm can automatically synthesize translation operators
requiring operations, where is the expansion order and
is dimension, compared with operations in a naive
approach carried out on (Cartesian) Taylor expansions. This is achieved by
using a compression scheme that asymptotically reduces the number of terms in
the Taylor expansion and then operating directly on this ``compressed''
representation. Judicious exploitation of shared subexpressions permits
formation, translation, and evaluation of local and multipole expansions to be
performed in operations, while an FFT-based scheme permits
multipole-to-local translations in operations. We
demonstrate computational scaling of code generation and evaluation as well as
numerical accuracy through numerical experiments on a number of potentials from
classical physics
The dark side of online transition of exams in higher education: a perspective of an emerging nation
No full textPurposeHigher educational institutes (HEIs) are experiencing a significant shift towards online education, which has been fast-forwarded with the global pandemic of COVID-19. The forced shift has also exposed many vulnerabilities in online education, especially assessments. The purpose of this study is to investigate the potential dark side of the digital transformation of examinations through the lens of university students.Design/methodology/approachThis study involves a sample of 127 university students from the fields of business and science, technology, education and management (STEM) and the key factors affecting student perception were assessed quantitatively to explore the interrelationships.FindingsResults revealed that both business and STEM students have a similar impression of the use of online examinations, and the majority still have mixed feelings about them as a replacement for physical examinations. The regrouping of the factors revealed two key dimensions, trustworthiness and apprehensible education, as key areas of student perception in the context of online examinations.Research limitations/implicationsThis study aims to strengthen the understanding of Kolb’s experiential learning mechanism through a discussion on the importance of abstract conceptualization as opposed to concrete experience in the establishment of the online assessment and learning space. Practically speaking, increasing investment in internet infrastructure and forming strategic alliances with important parties, like internet providers, to create uninterrupted network coverage, are an effective place to start if one wants to make sure that the process of moving to online learning is becoming more and more accepted by educators, students, and the general public.Originality/valueThe online transition to higher education has seen expedited growth since the pandemic and has not given much room for many HEIs globally to adjust. The procedures and techniques implemented take a Western lens, and less attention is given to the emerging context and its context-specific characteristics in such implementation. This study takes the theoretical lens of Kolb and proposes the key learnings for a successful online transition to assessment in emerging contexts
haesleinhuepf/apoc: 0.8.0
No full textChanged
The default value for num_ensembles has been increased to 100 and is now in line with scikit-learn defaults. This higher number of trees makes results more reproducible
flatsurf/exact-real: 2.0.0
No full textRemoved:
Removed the number field constructors that use the old e-antic API
(1.0.0-rc.1 to 1.0.0-rc.13.) This is a breaking API & ABI change.
Fixed:
Adapted to upstream changes in the latest e-antic rc
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