CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes
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Computing educators face significant challenges in providing timely support to students, especially in large class settings. Large language models (LLMs) have emerged recently and show great promise for providing on-demand help at a large scale, but there are concerns that students may over-rely on the outputs produced by these models. In this paper, we introduce CodeHelp, a novel LLM-powered tool designed with guardrails to provide on-demand assistance to programming students without directly revealing solutions. We detail the design of the tool, which incorporates a number of useful features for instructors, and elaborate on the pipeline of prompting strategies we use to ensure generated outputs are suitable for students. To evaluate CodeHelp, we deployed it in a first-year computer and data science course with 52 students and collected student interactions over a 12-week period. We examine students' usage patterns and perceptions of the tool, and we report reflections from the course instructor and a series of recommendations for classroom use. Our findings suggest that CodeHelp is well-received by students who especially value its availability and help with resolving errors, and that for instructors it is easy to deploy and complements, rather than replaces, the support that they provide to students.Future supercomputing demands that large scale parallel algorithms and applications have good scalability. Previous scalability studies lay stress on the studies of the algorithms scalability,but few on that of the application programs. They couldn't give users the information about how to adjust programs to improve its performance. The numerical scalability and parallel scalability are provided to describe whether the parallel system maintains its numerical attributes and parallel attributes. Furthermore, a suit of scalability evaluation criterion is provided to help the user to find the reason causing the bad scalability and to modify programs. This criterion and the near optimal scalability method are used to analyze the scalability of a large scale application program, namely two\|dimensional electromagnetic plasma with particle in cell method. Results show that the criteria help to locate the reason why the scalability is bad, and that the near optimal scalability method provides an approach to predict how many processors are to be used by a larger problem to get a reasonable utility, where its time is near to the shortest time to run and its efficiency is much improved.
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In this paper, the scalability of MINs in parallel machines is studied. Any desired number of measurable MINs performance metrics can participate in the scalability evaluation. Parallel algorithm scalability is an important issue as a system can be scalable for a certain number of algorithms and nonscalable for some others. Only one communication pattern is tested in this paper. The results obtained for scalability analysis for that particular communication pattern is very promising and will help a designer of a MIN to optimize its architecture parameters. The UPF factor is used to evaluate and compare the scalability of two MINs families: delta and oversized delta networks.
Factor (programming language)
Multistage interconnection networks
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Abstract The property of scalability for a given system indicates the ability of a system or a subsystem to be modified with changing load on the system. For a sufficiently large complex system, there are several factors that influence the ability of the system to scale. It is necessary to incorporate solutions to these factors (or bottlenecks) in the design for scalability of a given system. In this paper, we discuss such design principles to handle the key factors that influence the scalability of large complex systems. Specifically, we demonstrate design and implementation of simple, innovative, and relatively less expensive methodology to guarantee that a large complex system (such as network of sensors) is scalable under varying load conditions.
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Prevailing wisdom holds that for high-performance computation on parallel computers, scalable algorithms are good and unscalable ones are bad. But scalability is a slippery concept. It is difficult to give scalability a satisfactory, mathematically precise definition. In fact, the term scalable is used in three different senses: architectural scalability, scalability analysis of algorithms, and scalability studies of program performance. We shall be concerned mostly with the last two.
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For the CFD parallel applications,peopleoftenpartitiongridtogetmoreaccurate result,thuscauselargescalesize,whichdemandtheapplicationsforthe good scalability. We put forward three scalability metric, namely, the time scalability metric, the efficiency scalability metric, and the convergence scalability metric, to measure the variance of performance of the parallel applications. We use them to analyze the scalability of several CFD applications. Results show that these new scalability metrics overcome the disadvantages of the previous metrics, which are hard to distinguish the scalability differences caused by the various algorithm implements, and can be easy measured.
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This paper discusses scalability and outlines a specific approach to measuring the scalability of parallel computer systems. The relationship between scalability and speedup is described. It is shown that a parallel system is scalable for a given algorithm if and only if its speedup is unbounded. A technique is proposed that can be used to help determine whether a candidate model is correct, that is, whether it adequately approximates the system's scalability. Experimental results illustrate this technique for both a poorly scalable and a very scalable system.
Speedup
Parallel processing
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Scalability is one of the most important indicators to for performance of cloud storage system. In this paper, firstly, we pointed out limitations of three classical scalability evaluation methods with conditions of iso-speed, iso-efficiency and iso-ratio of parallel overhead to computation, when describing scalability. Then, we proposed a new scalability evaluation model which was suitable for cloud storage environment and named as EP scalability model. The EP scalability model directly reflected the performance of cloud storage system when the system scale and storage tasks expand.
Cloud storage
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Performance metric
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The aim of this paper is to present a method based on the Isoefficiency for assessing the scalability in big data environments. The programs word count and sort were implemented and compared in Hadoop and Spark. The results confirm that isoefficiency presented a linear growth as the size of the data sets was increased. It was experimentally confronted that the evaluated frameworks are scalable and a model of the form Y (s) = β X(s)$ where β ≈[0.47-0.85] <1 was obtained. The paper discuss how the scalability in big data is governed by a constant of scalability (β).
SPARK (programming language)
Constant (computer programming)
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In last decade crypto currencies become popular as there is no third party involvement while doing the transactions. Blockchain is the technology for using crypto-currencies. It attracts the attention of researchers and academicians , along with different features of Blockchain it is having the major issue of scalability which can be categorized into throughput , cost, capacity and networking . Improvement in Scalability affects the application of blockchain in business . Scalability affects due to some other factors like block interval time and block size which also may reduce the security . System may become vulnerable to different attacks if we blindly modify the scalability .In this paper we analyze the different ways to improve the scalability then we compare the features of blockchain with respect to different algorithms used to solve the scalability issue.
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