| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 515 | 50 | 53 | 11 |
The main points discussed in ACM Transactions on Parallel Computing deals with Parallel computing, Engineering ethics, Unit cost, Set (abstract data type) and Node (circuits). The featured works in Parallel algorithm and Parallelism (grammar), which all belong in the domain if Parallel computing, also overlaps with concepts under Nested parallelism, Open problem and Variety (cybernetics). It served as a forum through which researchers explored works on Unit cost in conjunction with disciplines such as Auxiliary memory, Bandwidth (signal processing), Transfer (computing), Data structure and Affine transformation.
Insufficient data to conduct the analysis
The top authors publishing in ACM Transactions on Parallel Computing (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in ACM Transactions on Parallel Computing (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 16.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 100.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 0.00% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Understanding the research landscape of ACM Transactions on Parallel Computing enables us to apply these computational methods in various disciplines. One particularly beneficial use case is in the field of education, more specifically in teacher training programs. For instance, applying parallel computing principles to manage, organize, and analyze educational data could enhance the efficiency of educational institutions and teacher performances.
In fact, several teaching credential programs in Alaska have started integrating parallel computing courses into their curriculums. This initiative allows future educators to leverage advanced computational methods for optimizing classroom teaching techniques and improving student learning outcomes.
By investigating methodologies and theories studied in ACM Transactions on Parallel Computing, educators can integrate valuable aspects of parallel computing into their teaching methodologies. The potential power of parallel computing in the field of education lies within its ability to process, analyze, and visualize multivariate and complex data about student learning and behavior
Laxman Dhulipala;Guy E. Blelloch;Julian Shun
(2021)Hartwig Anzt;Terry Cojean;Chen Yen-Chen;Jack Dongarra
(2020)Kartik Lakhotia;Rajgopal Kannan;Sourav Pati;Viktor Prasanna
(2020)Soheil Behnezhad;Laxman Dhulipala;Hossein Esfandiari;Jakub Łącki
(2021)Faith Ellen;Barun Gorain;Avery Miller;Andrzej Pelc
(2021)Tal Ben-Nun;Michael Sutton;Sreepathi Pai;Keshav Pingali
(2020)Christie Alappat;Achim Basermann;Alan R. Bishop;Holger Fehske
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