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SplashKit: A Development framework for Motivating and Engaging Students in Introductory programming

SplashKit: A Development Framework for Motivating and Engagi...

IEEE International Conference on Teaching, Assessment, and Learning for Engineering (IEEE TALE) - Engineering Next-Generation Learning

作者： Renzella, Jake Cummaudo, Alex Cain, Andrew Grundy, John Meyers, Jonathon Deakin Univ Sch Informat Technol Geelong Vic Australia Deakin Univ Appl Artificial Intelligence Inst Geelong Vic Australia Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9781538665220

Learning to program is known to be challenging for many students. Upon entry, students often have poor perceptions of their capabilities with some anxiety around the challenges they expect to face in learning to code. Lowering the barriers to entry will help ease students into programming and enable a broader range of student to continue programming. SplashKit is an educationally focused development framework designed to aid the teaching of programming by empowering students to create interesting and dynamic programs from their first programming tasks. This paper explores how SplashKit can be used in tertiary education to underpin a range of introductory programming approaches.

关键词： programming framework programming education introductory programming motivating programming students

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framework Design for Improving Computational Efficiency and programming Productivity for Distributed Machine Learning

Framework Design for Improving Computational Efficiency and ...

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作者： Jin Kyu Kim Carnegie Mellon University

学位级别：博士

Machine learning (ML) methods are used to analyze data in a wide range of areas, such as finance, e-commerce, medicine, science, and engineering, and the size of machine learning problems has grown very rapidly in terms of data size and model size in the era of big data. This trend drives industry and academic communities toward distributed machine learning that scales out ML training in a distributed system for completion in a reasonable amount of time. There are two challenges in implementing distributed machine learn- ing: computational efficiency and programming productivity. The traditional data-parallel approach often leads to suboptimal training performance in dis- tributed ML due to data dependencies among model parameter updates and nonuniform convergence rates of model parameters. From the perspective of an ML programmer, distributed ML programming requires substantial devel- opment overhead even with high-level frameworks because they require an ML programmer to switch to a different mental model for programming from a familiar sequential programming model. The goal of my thesis is to improve the computational efficiency and pro- gramming productivity of distributed machine learning. In an efficiency study, I explore model update scheduling schemes that consider data dependencies and nonuniform convergence speeds of model parameters to maximize con- vergence per iteration and present a runtime system STRADS that efficiently execute model update scheduled ML applications in a distributed system. In a productivity study, I present familiar sequential-like programming API that simplifies conversion of a sequential ML program into a distributed program without requiring an ML programmer to switch to a different mental model for programming and implement a new runtime system STRADS-Automatic Parallelization(AP) that efficiently executes ML applications written in our API in a distributed system.

关键词： Distributed Systems Large-Scale Machine Learning programming framework Computer Science

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Cloud-based Activity-aaService cyber-physical framework for human activity monitoring in mobility

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 75卷 158-171页

作者： Gravina, Raffaele Ma, Congcong Pace, Pasquale Aloi, Gianluca Russo, Wilma Li, Wenfeng Fortino, Giancarlo Univ Calabria DIMES Via P Bucci I-87036 Arcavacata Di Rende CS Italy CNR Natl Res Council Italy Inst High Performance Comp & Networking ICAR Via P Bucci 7-11C I-87036 Arcavacata Di Rende CS Italy Wuhan Univ Technol Sch Logist Engn Wuhan 430070 Peoples R China

This paper proposes Activity as a Service (Activity-aaService), a full-fledged cyber-physical framework to support community, on-line and off-line human activity recognition and monitoring in mobility. Activity-aaService is able to address the current lack of Cloud-Assisted Body Area Networks platforms and applications supporting monitoring and analysis of human activity for single individuals and communities. Activity-aaService is built atop the BodyCloud platform so enabling efficient BSN-based sensor data collection and local processing (Body-side), high performance computing of collected sensor data and data storing on the Cloud (Cloud-side), workflow-based programming of data analysis (Analyst-side), and advanced visualization of results (Viewer-side). Specifically, it provides specific, powerful and flexible programming abstractions for the rapid prototyping of efficient human activity-oriented applications. The effectiveness of the proposed framework has been demonstrated through the development of several prototypes related to physical activity monitoring, step counting, physical energy estimation, automatic fall detection, and smart wheelchair support. Finally, performance evaluation of the proposed framework at the Body-side of the activity classification has been carried out by analyzing processing load, data transmission time, CPU usage, memory footprint, and battery consumption using four heterogeneous mobile devices representing low, medium and high performance mobile platforms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Cloud computing Activity monitoring Wearable sensors programming framework Software as a service

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Trigger-Based Incremental Data Processing with Unified Sync and Async Model

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2021年第1期9卷 372-385页

作者： Dai, Dong Chen, Yong Kimpe, Dries Ross, Robert B. Texas Tech Univ Dept Comp Sci Lubbock TX 79413 USA Argonne Natl Lab Math & Comp Sci Div Chicago IL 60439 USA

In recent years, more and more applications in the cloud have needs to process large-scale on-line datasets, which evolve over time as new entries are added and existing entries are modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient processing with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications' developers, which further significantly restricts their usabilities. In this study, we propose a trigger-based incremental computing framework in the cloud, called Domino, with both synchronous and asynchronous mechanisms to coordinate parallel triggers. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and extensive evaluation results confirm that it can deliver sufficient performance, and also is easy to use for incremental applications in large-scale distributed computing.

关键词： programming framework cloud incremental computing

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Multiscale Universal Interface: A concurrent framework for coupling heterogeneous solvers

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JOURNAL OF COMPUTATIONAL PHYSICS 2015年 297卷 13-31页

作者： Tang, Yu-Hang Kudo, Shuhei Bian, Xin Li, Zhen Karniadakis, George Em Brown Univ Div Appl Math Providence RI 02912 USA Kobe Univ Grad Sch Syst Informat Naka Ku Kobe Hyogo 6578501 Japan Pacific NW Natl Lab Collaboratory Math Mesoscop Modeling Mat Richland WA 99354 USA

Concurrently coupled numerical simulations using heterogeneous solvers are powerful tools for modeling multiscale phenomena. However, major modifications to existing codes are often required to enable such simulations, posing significant difficulties in practice. In this paper we present a C++ library, i.e. the Multiscale Universal Interface (MUI), which is capable of facilitating the coupling effort for a wide range of multiscale simulations. The library adopts a header-only form with minimal external dependency and hence can be easily dropped into existing codes. A data samplerconcept is introduced, combined with a hybrid dynamic/static typing mechanism, to create an easily customizable framework for solver-independent data interpretation. The library integrates MPI MPMD support and an asynchronous communication protocol to handle inter-solver information exchange irrespective of the solvers' own MPI awareness. Template metaprogramming is heavily employed to simultaneously improve runtime performance and code flexibility. We validated the library by solving three different multiscale problems, which also serve to demonstrate the flexibility of the framework in handling heterogeneous models and solvers. In the first example, a Couette flow was simulated using two concurrently coupled Smoothed Particle Hydrodynamics (SPH) simulations of different spatial resolutions. In the second example, we coupled the deterministic SPH method with the stochastic Dissipative Particle Dynamics (DPD) method to study the effect of surface grafting on the hydrodynamics properties on the surface. In the third example, we consider conjugate heat transfer between a solid domain and a fluid domain by coupling the particle-based energy-conserving DPD (eDPD) method with the Finite Element Method (FEM). (C) 2015 Elsevier Inc. All rights reserved.

关键词： Multiscale modeling Concurrent coupling Multiphysics simulation Energy-conserving DPD SPH FEM programming framework

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programming for scientific computing on peta-scale heterogeneous parallel systems

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Journal of Central South University 2013年第5期20卷 1189-1203页

作者：杨灿群吴强唐滔王锋薛京灵 State Key Laboratory of High Performance Computing(National University of Defense Technology) School of Computer Science and Engineering University of New South Wales

Peta-scale high-perfomlance computing systems are increasingly built with heterogeneous CPU and GPU nodes to achieve higher power efficiency and computation throughput. While providing unprecedented capabilities to conduct computational experiments of historic significance, these systems are presently difficult to program. The users, who are domain experts rather than computer experts, prefer to use programming models closer to their domains （e.g., physics and biology） rather than MPI and OpenME This has led the development of domain-specific programming that provides domain-specific programming interfaces but abstracts away some performance-critical architecture details. Based on experience in designing large-scale computing systems, a hybrid programming framework for scientific computing on heterogeneous architectures is proposed in this work. Its design philosophy is to provide a collaborative mechanism for domain experts and computer experts so that both domain-specific knowledge and performance-critical architecture details can be adequately exploited. Two real-world scientific applications have been evaluated on TH-IA, a peta-scale CPU-GPU heterogeneous system that is currently the 5th fastest supercomputer in the world. The experimental results show that the proposed framework is well suited for developing large-scale scientific computing applications on peta-scale heterogeneous CPU/GPU systems.

关键词： heterogeneous parallel system programming framework scientific computing GPU computing molecular dynamic

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Vulkan all the way: Transitioning to a modern low-level graphics API in academia

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COMPUTERS & GRAPHICS-UK 2023年 111卷 155-165页

作者： Unterguggenberger, Johannes Kerbl, Bernhard Wimmer, Michael TU Wien Inst Visual Comp & Human Ctr Technol Favoritenstr 9-11 E193-02 A-1040 Vienna Austria

For over two decades, the OpenGL API provided users with the means for implementing versatile, feature-rich, and portable real-time graphics applications. Consequently, it has been widely adopted by practitioners and educators alike and is deeply ingrained in many curricula that teach real-time graphics for higher education. Over the years, the architecture of graphics processing units (GPUs) incrementally diverged from OpenGL's conceptual design. The more recently introduced Vulkan API provides a more modern, fine-grained approach for interfacing with the GPU, which allows a high level of controllability and, thereby, deep insights into the inner workings of modern GPUs. This property makes the Vulkan API especially well suitable for teaching graphics programming in university education, where fundamental knowledge shall be conveyed. Hence, it stands to reason that educators who have their students' best interests at heart should provide them with corresponding lecture material. However, Vulkan is notoriously verbose and rather challenging for first-time users, thus transitioning to this new API bears a considerable risk of failing to achieve expected teaching goals. In this paper, we document our experiences after teaching Vulkan in both introductory and advanced graphics courses side-by-side with conventional OpenGL. A collection of surveys enables us to draw conclusions about perceived workload, difficulty, and students' acceptance of either approach. In doing so, we identify suitable conditions and recommendations for teaching Vulkan to both undergraduate and graduate students. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Real-time rendering GPU Graphics API Teaching Vulkan programming framework

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A general and fast distributed system for large-scale dynamic programming applications

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PARALLEL COMPUTING 2016年 60卷 1-21页

作者： Wang, Chen Yu, Ce Tang, Shanjiang Xiao, Jian Sun, Jizhou Meng, Xiangfei Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China Natl Supercomp Ctr Tianjin Peoples R China

Dynamic programming is an important technique widely used in many scientific applications. Due to the massive volume of applications' data in practice, parallel and distributed DP is a must. However, writing a parallel and distributed DP program is difficult and error prone because of its intrinsically strong data dependency. In this paper, we present DPX10, a DAG-based distributed X10 framework aiming at simplifying the parallel programming for DP applications. DPX10 enables users to write highly efficient parallel DP programs without much effort. For DPX10 programming, users only need to do two things: 1) Instantiating a DAG pattern by indicating the dependency between vertices of the DAG;2) Implementing the DP application's logic in the compute method of the vertices. DPX10 provides eight commonly used DAG patterns and a simple API to allow users to customize their own DAG patterns. All the tiresome work of DP parallelization including DAG distribution, tasks scheduling, and tasks communication are hidden from users and covered by DPX10. Moreover, DPX10 is fault-tolerant and has a mechanism to handle the problem of straggler tasks, which run much slower than other tasks due to unexpected resource contention. Finally, we use four DP applications with up to 2 billion vertices running on 240 cores to demonstrate the simplicity, efficiency, and scalability of our proposed framework. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Dynamic programming programming framework X10 APGAS

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MALOC: A Fully Pipelined FPGA Accelerator for Convolutional Neural Networks With All Layers Mapped on Chip

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2018年第11期37卷 2601-2612页

作者： Gong, Lei Wang, Chao Li, Xi Chen, Huaping Zhou, Xuehai Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Sch Software Engn Hefei 230027 Anhui Peoples R China

Recently, field-programmable gate arrays (FPGAs) have been widely used in the implementations of hardware accelerator for convolutional neural networks (CNNs). However, most of these existing accelerators are designed in the same idea as their ASIC counterparts, in which all operations from different layers are mapped to the same hardware units and working in a multiplexed way. This manner does not take full advantage of reconfigurability and customizability of FPGAs, resulting in a certain degree of computational efficiency degradation. In this paper, we propose a new architecture for FPGA-based CNN accelerator that maps all the layers to their own on-chip units and working concurrently as a pipeline. A comprehensive mapping and optimizing methodology based on establishing roofline model oriented optimization model is proposed, which can achieve maximum resource utilization as well as optimal computational efficiency. Besides, to ease the programming burden, we propose a design framework which can provide a one-stop function for developers to generate the accelerator with our optimizing methodology. We evaluate our proposal by implementing different modern CNN models on Xilinx Zynq-7020 and Virtex-7 690t FPGA platforms. Experimental results show that our implementations can achieve a peak performance of 910.2 GOPS on Virtex-7 690t, and 36.36 GOP/s/W energy efficiency on Zynq-7020, which are superior to the previous approaches.

关键词： Convolutional neural network (CNN) design space exploration (DSE) field-programmable gate array (FPGA)-based accelerator pipeline programming framework redundancy elimination

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Markov decision process based time-varying optimal control for colloidal self-assembly

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IFAC-PapersOnLine 2016年第7期49卷 430-435页

作者： Tang, Xun A. Bevan, Michael Grover, Martha A. Georgia Institute of Technology AtlantaGA30332 United States Johns Hopkins University BaltimoreMD21218 United States

Crystals made of periodically well-ordered nano- and/or micro-scale elements can interact with light to give novel properties. These perfect crystals have applications in a wide range of areas. For example, invisibility cloaks that reroute light transmission make objects disappear. However, manufacturing such perfect crystals still remains challenging. Here, we propose a low-dimensional Markov decision process based dynamic programming framework to optimally control a colloidal self-assembly process for perfect crystal fabrication. Based on the simulation results, we demonstrate that an open-loop control policy identified with the proposed framework is able to reduce the defective assemblies from 46% of uncontrolled to 8% of controlled production. Moreover, when feedback is available, a closed-loop optimal finite-horizon control policy can further reduce the defective assemblies down to 5% out of 100 independent simulation runs. © 2016

关键词： Feedback Control theory Dynamic programming Feedback control Invisibility cloaks Light transmission Markov processes Self assembly Stochastic systems Colloidal self assembly Low dimensional Markov Decision Processes Open loop control Optimal controls Perfect crystals programming framework Stochastic control

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