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Does AP CS principles Broaden Participation in computing? An Analysis of APCSA and APCSP Participants 20

Does AP CS Principles Broaden Participation in Computing? An...

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51st acm SIGCSE Technical symposium on Computer Science Education (SIGCSE)

作者： Sax, Linda J. Newhouse, Kaitlin N. S. Goode, Joanna Skorodinsky, Max Nakajima, Tomoko M. Sendowski, Michelle UCLA Grad Sch Educ & Informat Studies Los Angeles CA 90095 USA Univ Oregon Dept Educ Studies Eugene OR USA

ISBN: (纸本)9781450367936

A major attempt to broaden participation in computer science has centered on the design and development of a new high school Advanced Placement (AP) course, AP Computer Science principles (CSP). This course was created to intentionally engage a wider and more diverse group of students in learning about computing than those who had historically enrolled in programming-focused AP Computer Science "A" (CSA). After several years in the pilot phase, the course was officially offered by the College Board in 2016-17. This paper uses nationwide Freshman Survey data to examine the demographics and characteristics of students who took either CSA, the new CSP course, or both courses by the time they entered college in Fall 2017. Using crosstabs, z-tests, and one-way ANOVA, this study uncovered the similarities and differences between students who had participated in one or both of these courses. Our findings suggest that while students who took only CSP were more diverse than those who took only CSA, they exhibited less computing confidence and less interest in computing majors and tech careers. This study suggests that while CSP may be recruiting more females and racially diverse students into the course, this course alone may not serve as a direct pipeline into computing majors and careers.

关键词： AP Computer Science A Course/Exam AP Computer Science principles Course/Exam Gender and Diversity

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On distributed Listing of Cliques 20

On Distributed Listing of Cliques

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39th symposium on principles of distributed computing

作者： Censor-Hillel, Keren Le Gall, Francois Leitersdorf, Dean Technion Dept Comp Sci Haifa Israel Nagoya Univ Grad Sch Math Nagoya Aichi Japan

ISBN: (纸本)9781450375825

We show an (O) over tilde (n(p/(p+2)))-round algorithm in the CONGEST model for listing of K-p (a clique with p nodes), for all p = 4, p >= 6. For p = 5, we show an (O) over tilde (n(3/4))-round algorithm. For p = 4 and p = 5, our results improve upon the previous state-of-the-art of O(n(5/6+o(1))) and O(n(21/22+o(1))), respectively, by Eden et al. [DISC 2019]. For all p >= 6, ours is the first sub-linear round algorithm for Kp listing. We leverage the recent expander decomposition algorithm of Chang et al. [SODA 2019] to create clusters with a good mixing time. Three key novelties in our algorithm are: (1) we carefully iterate our listing process with coupled values of min-degree within the clusters and arboricity outside the clusters, (2) all the listing is done within the cluster, which necessitates new techniques for bringing into the cluster the information about all edges that can potentially form K-p instances with the cluster edges, and (3) within each cluster we use a sparsity-aware listing algorithm, which is faster than a general listing algorithm and which we can allow the cluster to use since we make sure to sparsify the graph as the iterations proceed. As a byproduct of our algorithm, we show an optimal sparsityaware algorithm for K-p listing, which runs in Theta(1 + m/n(1+2/p)) rounds in the CONGESTED CLIQUE model. Previously, Pandurangan et al. [SPAA 2018], Chang et al. [SODA 2019], and Censor-Hillel et al. [TCS 2020] showed sparsity-aware algorithms for the case of p = 3, yet ours is the first such sparsity aware algorithm for p >= 4.

关键词： Clustering algorithms

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Towards HPC I/O Performance Prediction Through Large-scale Log Analysis 20

Towards HPC I/O Performance Prediction Through Large-scale L...

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29th International Conference on High-Performance Parallel and distributed computing (HPDC)

作者： Kim, Sunggon Sim, Alex Wu, Kesheng Byna, Suren Son, Yongseok Eom, Hyeonsang Seoul Natl Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Chung Ang Univ Seoul South Korea

ISBN: (纸本)9781450370523

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.

关键词： High Performance computing distributed File System I/O Performance Prediction Log Analysis

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Differential Privacy in distributed Environments: An Overview and Open Questions 21

Differential Privacy in Distributed Environments: An Overvie...

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proceedings of the 2021 acm symposium on principles of distributed computing

作者： Cynthia Dwork Harvard University & Radcliffe Institute for Advanced Study Cambridge MA USA

ISBN: (纸本)9781450385480

Differential privacy is a mathematically rigorous definition of privacy tailored to statistical analysis of large datasets. Differentially private algorithms are equipped with a parameter which controls the formal measure of privacy loss. All algorithms have utility/privacy tradeoffs, and the goal of algorithmic research in differential privacy is to optimize this *** privacy is most widely studied in the centralized model, in which a trusted and trustworthy curator has access to raw data. Deployment in industry has focused on the local model, where privacy is "rolled into" the data on the client via randomization before being collected. There is a separation between the power of the centralized and local *** a brief recap of differential privacy and its properties, we will survey a few highlights of differential privacy in a variety of distributed settings that lie between the local and centralized models, and conclude with suggestions for future research.

关键词： differential privacy distributed algorithms

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Beyond CS principles: Bringing the Frontiers of computing to K12 21

Beyond CS Principles: Bringing the Frontiers of Computing to...

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proceedings of the 52nd acm Technical symposium on Computer Science Education

作者： Ákos Lédeczi Shuchi Grover Veronica Catete Brian Broll Vanderbilt University Nashville TN USA Looking Glass Venture Palo Alto CA USA North Carolina State University Raleigh NC USA

ISBN: (纸本)9781450380621

The AP Computer Science principles (CSP) high school course introduces students to computer science and programming. What should motivated students study after successful completion of AP CSP? The AP CSA class teaches Java programming and it has traditionally not attracted students from underrepresented groups. We are working on an alternative, projects-based course that will teach cutting edge CS concepts, such as distributed computing, computer networking, cybersecurity, the internet of things and machine learning, in a hands-on, accessible manner. Such an approach enables students to work on problems that interest them making computing more relevant and the curriculum more engaging. We utilize NetsBlox, a collaborative, block-based programming environment that extends Snap! with a few carefully selected abstractions that open up the vast array of resources freely available on the internet for student programs. Moreover, the tool enables students to work together on the same project remotely similarly to how Google Docs operate. This demonstration will introduce the environment and highlight its utility in creating distributed applications such as a shared whiteboard app and projects that access public domain scientific data sources and visualize them in various ways using online services such as Google Maps or charting. More information is available at https://***.

关键词： distributed computing block-based programming internet of things computer networking cybersecurity

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DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems 20

DCDB Wintermute: Enabling Online and Holistic Operational Da...

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29th International Conference on High-Performance Parallel and distributed computing (HPDC)

作者： Netti, Alessio Mueller, Micha Guillen, Carla Ott, Michael Tafani, Daniele Ozer, Gence Schulz, Martin Tech Univ Munich Leibniz Supercomp Ctr Munich Germany Leibniz Supercomp Ctr Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9781450370523

As we approach the exascale era, the size and complexity of HPC systems continues to increase, raising concerns about their manageability and sustainability. For this reason, more and more HPC centers are experimenting with fine-grained monitoring coupled with Operational Data Analytics (ODA) to optimize efficiency and effectiveness of system operations. However, while monitoring is a common reality in HPC, there is no well-stated and comprehensive list of requirements, nor matching frameworks, to support holistic and online ODA. This leads to insular ad-hoc solutions, each addressing only specific aspects of the problem. In this paper we propose Wintermute, a novel generic framework to enable online ODA on large-scale HPC installations. Its design is based on the results of a literature survey of common operational requirements. We implement Wintermute on top of the holistic DCDB monitoring system, offering a large variety of configuration options to accommodate the varying requirements of ODA applications. Moreover, Wintermute is based on a set of logical abstractions to ease the configuration of models at a large scale and maximize code re-use. We highlight Wintermute's flexibility through a series of practical case studies, each targeting a different aspect of the management of HPC systems, and then demonstrate the small resource footprint of our implementation.

关键词： High-Performance computing Monitoring Operational Data Analytics System Management Online Analysis

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proceedings of the Annual acm symposium on principles of distributed computing

Proceedings of the Annual ACM Symposium on Principles of Dis...

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35th acm symposium on principles of distributed computing, PODC 2016

ISBN: (纸本)9781450339643

The proceedings contain 65 papers. The topics discussed include: the greedy spanner is existentially optimal;distributed algorithms for planar networks i: planar embedding;brief announcement: labeling schemes for power-law graphs;brief announcement: sublinear-space distance labeling using hubs;brief announcement: optimal leader election in multi-hop radio networks;brief announcement: the small world of curious beings;a randomized concurrent algorithm for disjoint set union;brief announcement: local independent set approximation;deterministic objects: life beyond consensus;unbeatable set consensus via topological and combinatorial reasoning;and a polylogarithmic gossip algorithm for plurality consensus.

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A deterministic almost-tight distributed algorithm for approximating single-source shortest paths

A deterministic almost-tight distributed algorithm for appro...

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作者： Henzinger, Monika Krinninger, Sebastian Nanongkai, Danupon Faculty of Computer Science University of Vienna Vienna1090 Austria Department of Computer Sciences University of Salzburg Salzburg5020 Austria Department of Theoretical Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We present a deterministic (1 + o(1))-approximation (n1/2+o(1) + D1+o(1))-time algorithm for solving the single-source shortest paths problem on distributed weighted networks (the CONGEST model);here n is the number of nodes in the network, D is its (hop) diameter, and edge weights are positive integers from 1 to poly(n). This is the first nontrivial deterministic algorithm for this problem. It also improves (i) the running time of the randomized (1 + o(1))-approximation Õ(√nD1/4 + D)-time algorithm of Nanongkai [in proceedings of STOC, 2014, pp. 565-573] by a factor of as large as n1/8, and (ii) the O(∊-1 log ∊ -1)-approximation factor of Lenzen and Patt-Shamir's Õ(n1/2+∊ + D)-time algorithm [in proceedings of STOC, 2013, pp. 381-390] within the same running time. (Throughout, we use Õ(·) to hide polylogarithmic factors in n.) Our running time matches the known time lower bound of Ω (√n/log n + D) [M. Elkin, SIAM J. Comput., 36 (2006), pp. 433-456], thus essentially settling the status of this problem which was raised at least a decade ago [M. Elkin, SIGACT News, 35 (2004), pp. 40-57]. It also implies a (2 + o(1))-approximation (n1/2+o(1) + D1+o(1))- time algorithm for approximating a network's weighted diameter which almost matches the lower bound by Holzer and Pinsker [in proceedings of OPODIS, 2015, Schloss Dagstuhl. Leibniz-Zent. Inform., Wadern, Germany, 2016, 6]. In achieving this result, we develop two techniques which might be of independent interest and useful in other settings: (i) a deterministic process that replaces the ``hitting set argument"" commonly used for shortest paths computation in various settings, and (ii) a simple, deterministic construction of an (no(1), o(1))-hop set of size n1+o(1). We combine these techniques with many distributed algorithmic techniques, some of which are from problems that are not directly related to shortest paths, e.g., ruling sets [A. V. Goldberg, S. A. Plotkin, and G. E. Shannon, SIAM J. Discrete Math., 1 (1988),

关键词： Approximation algorithms

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Dynamic scaling for low-precision learning 21

Dynamic scaling for low-precision learning

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proceedings of the 26th acm SIGPLAN symposium on principles and Practice of Parallel Programming

作者： Ruobing Han Min Si James Demmel Yang You Peking University Argonne National Laboratory UC Berkeley National University of Singapore

ISBN: (纸本)9781450382946

In recent years, distributed deep learning is becoming popular in industry and academia. Although researchers want to use distributed systems for training, it has been reported that the communication cost for synchronizing gradients can be a bottleneck. Using low-precision gradients is a promising technique for reducing the bandwidth requirement. In this work, we propose Auto Precision Scaling (APS), an algorithm that can improve the accuracy when we communicate gradients by low-precision floating-point values. APS can improve the accuracy for all precisions with a trivial communication cost. Our experimental results show that for both image classification and segmentation, applying APS can train the state-of-the-art models by 8-bit floating-point gradients with no or only a tiny accuracy loss (<0.05%). Furthermore, we can avoid any accuracy loss by designing a hybrid-precision technique. Finally, we propose a performance model to evaluate the proposed method. Our experimental results show that APS can get a significant speedup over the state-of-the-art method. To make it available to researchers and developers, we design and implement a high-performance system for customized precision Deep Learning(CPD), which can simulate the training process using an arbitrary low-precision customized floating-point format. We integrate CPD into PyTorch and make it open-source to the public1.

关键词： distributed training

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Plain SINR is Enough! 19

Plain SINR is Enough!

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38th acm symposium on principles of distributed computing (PODC)

作者： Halldorsson, Magnus M. Tonoyan, Tigran Reykjav Univ Dept Comp Sci ICE TCS Reykjavik Iceland

ISBN: (纸本)9781450362177

We develop randomized distributed algorithms for many of the most fundamental communication problems in the wireless SINR model, including (multi-message) broadcast, local broadcast, coloring, MIS, and aggregation. The complexity of the algorithms is optimal up to polylogarithmic preprocessing time. It shows - contrary to expectation - that the plain vanilla SINR model is just as powerful and fast (modulo the preprocessing) as various extensions studied, including power control, carrier sense, collision detection, free acknowledgements, and GPS location. A key component of the algorithms is an efficient simulation of Congest algorithms on a constant-density SINR backbone.

关键词： wireless network distributed algorithm plain SINR

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