NSF/IEEE-TCPP Curriculum on Parallel and Distributed Computing for Undergraduates - Version II - Big Data, Energy, and Distributed Computing

被引:0
|
作者
Prasad, Sushil [1 ]
Weems, Charles [2 ]
Sussman, Alan [3 ]
Gupta, Anshul [4 ]
Estrada, Trilce [5 ]
Vaidyanathan, Ramachandran [6 ]
Ghafoor, Sheikh [7 ]
Kant, Krishna [8 ]
Stunkel, Craig [9 ]
机构
[1] Univ Texas San Antonio, San Antonio, TX 78249 USA
[2] Univ Massachusetts, Amherst, MA 01003 USA
[3] Univ Maryland, College Pk, MD 20742 USA
[4] IBM Res, Yorktown Hts, NY USA
[5] Univ New Mexico, Albuquerque, NM 87131 USA
[6] Louisiana State Univ, Baton Rouge, LA 70803 USA
[7] Tennessee Technol Univ, Cookeville, TN USA
[8] Temple Univ, Philadelphia, PA 19122 USA
[9] NVIDIA, St Louis, MO USA
基金
美国国家科学基金会;
关键词
Education; Undergraduate Curriculum; Parallel and Distributed Computing; Bloom's Classification; Learning Outcomes;
D O I
10.1145/3545947.3569594
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This special session will report on the updated NSF/IEEE-TCPP Curriculum on Parallel and Distributed Computing released in Nov 2020 by the Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER). The purpose of the special session is to obtain SIGCSE community feedback on this curriculum in a highly interactive manner employing the hybrid modality and supported by a full-time CDER booth for the duration of SIGCSE. In this era of big data, cloud, and multi- and many-core systems, it is essential that the computer science (CS) and computer engineering (CE) graduates have basic skills in parallel and distributed computing (PDC). The topics are primarily organized into the areas of architecture, programming, and algorithms topics. A set of pervasive concepts that percolate across area boundaries are also identified. Version 1 of this curriculum was released in December 2012. That curriculum guideline has over 140 early adopter institutions worldwide and has been incorporated into the 2013 ACM/IEEE Computer Science curricula. This Version-II represents a major revision. The updates have focused on enhancing coverage related to the topical aspects of Big Data, Energy, and Distributed Computing. The session will also report on related CDER activities including a workshop series on a PDC institute conceptualization, developing a CE-oriented version of the curriculum, and identifying a minimal set of PDC topics aligned with ABET's exposure-level PDC requirements. The interested SIGCSE audience includes educators, authors, publishers, curriculum committee members, department chairs and administrators, professional societies, and the computing industry.
引用
收藏
页码:1220 / 1221
页数:2
相关论文
共 50 条
  • [1] NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Core Topics for Undergraduates
    Prasad, Sushi K.
    Chtchelkanova, Almadena
    Gupta, Anshul
    Rosenberg, Arnold
    Sussman, Alan
    Weems, Charles
    PROCEEDINGS OF THE 45TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE'14), 2014, : 735 - 735
  • [2] NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Core Topics for Undergraduates
    Prasad, Sushil K.
    Chtchelkanova, Almadena
    Das, Sajal
    Dehne, Frank
    Gouda, Mohamed
    Gupta, Anshul
    Jaja, Joseph
    Kant, Krishna
    La Salle, Anita
    Lumsdaine, Manish
    Padua, David
    Parashar, Manish
    Prasanna, Viktor
    Robert, Yves
    Rosenberg, Arnold
    Sahni, Sartaj
    Shirazi, Behrooz
    Sussman, Alan
    Weems, Charles
    Wu, Jie
    SIGCSE 11: PROCEEDINGS OF THE 42ND ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, 2011, : 617 - 618
  • [3] NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Status Report
    Prasad, Sushil K.
    Weems, Charles C.
    Dougherty, John P.
    Deb, Debzani
    SIGCSE'18: PROCEEDINGS OF THE 49TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, 2018, : 134 - 135
  • [4] Parallel and distributed computing for Big Data applications
    Senger, Hermes
    Geyer, Claudio
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2016, 28 (08): : 2412 - 2415
  • [6] Experiences on Teaching Parallel and Distributed Computing for Undergraduates
    Saule, Erik
    2018 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW 2018), 2018, : 361 - 368
  • [7] Distributed Computing and Inference for Big Data
    Zhou, Ling
    Gong, Ziyang
    Xiang, Pengcheng
    ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, 2024, 11 : 533 - 551
  • [8] PARALLEL COMPUTING WITH DISTRIBUTED SHARED DATA
    HSU, MC
    PROCEEDINGS : FIFTH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, 1989, : 485 - 485
  • [9] Parallel and distributed computing for data mining
    Zomaya, AY
    El-Ghazawi, T
    Frieder, O
    IEEE CONCURRENCY, 1999, 7 (04): : 11 - 13
  • [10] Big data mining with parallel computing: A comparison of distributed and MapReduce methodologies
    Tsai, Chih-Fong
    Lin, Wei-Chao
    Ke, Shih-Wen
    JOURNAL OF SYSTEMS AND SOFTWARE, 2016, 122 : 83 - 92