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[서평]「Computer Organization and Design, The Hardware/Software Interface」

This paper presents a systematic literature review of the recent (2006--2010) development of automatic assessment tools for programming exercises. We discuss the major features that the tools support and the different approaches they are using both from the pedagogical and the technical point of view. Examples of these features are ways for the teacher to define tests, resubmission policies, security issues, and so forth. We have also identified a list of novel features, like assessing web software, that are likely to get more research attention in the future. As a conclusion, we state that too many new systems are developed, but also acknowledge the current reasons for the phenomenon. As one solution we encourage opening up the existing systems and joining efforts on developing those further. Selected systems from our survey are briefly described in Appendix A.

Review of recent systems for automatic assessment of programming assignments

The advent of information technologies to Universities has improved the teaching-learning process. Students can increase their learning skills using information technology. Those using the Moodle platform regularly seem to get better grades than those who rarely or never use it. This paper analyzes students' intention to use Moodle platforms to improve the teaching-learning process. Its main focus is to analyze the use of the Moodle platform by University students, identifying factors which might influence the intention to use it. Understanding the factors influencing the intention to use Moodle will allow us to determine which actions might be carried out to boost its use by University students, to therefore, improve both their skills and grades. The theoretical grounding for this research is the Technology Acceptance Model (TAM). TAM specifies the causal relationships between perceived usefulness, perceived ease of use and actual usage behavior. The proposed model has six constructs and nine hypotheses have been generated from the connections between these six constructs. These constructs include perceived compatibility with student tasks, perceived usefulness for professors, and training. Our results provide support for a number of relationships in the hypothesized model. In light of these findings, implications for theory and practice are discussed.

The acceptance of Moodle technology by business administration students

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer?Rao lower bound, certifying its own performance.

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Robust online Hamiltonian learning

2012 [AS13b, Wal12a]. 2013 [Alv13, Bar13, Cam13, Car13, CCS13, Cut13, Edi14, Swe13]. 2014 [CDS13, DN14, ND14, Rod14, Tym14a]. 2015 [DeL14a, DeL14b, She15b]. 2016 [AT16a, CCV16, CCV17, MR15, SDCT16a, SDCT16c, She16a]. 2017 [DG17, EC17, Fit18b, MJ18, TCSM17, TCM17]. 2018 [BG18b, DD17, Dec19b, Gol18, JI18, MKMP18, MMK18, PQH17, She18]. 2019 [Cut19, FI19, HPQ19, MP19a, MPRM19, MP19b, MS19, Rod18, She19a]. 2020 [DH19].

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A Complete Bibliography of Publications in SIGCSE Bulletin: 2000{2009

This work describes a new Moodle module developed to give support to the practical content of a basic computer organization course. This module goes beyond the mere hosting of resources and assignments. It makes use of an automatic checking and verification engine that works on the VHDL designs submitted by the students. The module automatically keeps up to date information about their state, and significantly reduces the overload that a continuous assessment demands to the teacher. Additionally, this new module is oriented to promote a collaborative teamwork allowing to define student teams in a more operative way than built-in Moodle groups. The module has been designed according to the Moodle philosophy and its application can be extended to other similar subjects.

A new Moodle module supporting automatic verification of VHDL-based assignments

This paper presents a new parallelization method for reductions of arrays with subscripted subscripts on scalable shared memory multiprocessors The mapping of computations is based on grouping reduction loop iterations into sets that are further assigned to the cooperating threads of computation Iterations belonging to the same set are chosen in such a way that update different entries in the reduction array That is, the loop distribution implies a conflict-free write distribution of the reduction array The iteration sets are set up by building a loop-index prefetching data structure that allows to reorder properly the loop iterations The proposed method is general, scalable, and easy to implement on a compiler In addition it deals in a uniform way with one and multiple subscript arrays In case of multiple indirection arrays, writes on the reduction array affecting different sets are solved by defining conflict-free supersets A performance evaluation is presented From the experimental results and performance analysis, the proposed method appears as a clear alternative to the array expansion and privatized buffer techniques, used on state-of-the-art parallelizing compilers, like Polaris or SUIF The scalability problem that those techniques exhibit is missing in our method, as the memory overhead presented does not depend on the number of processors

A compiler method for the parallel execution of irregular reductions in scalable shared memory multiprocessors

Time series analysis is a key technique for extracting and predicting events in domains as diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and more. Matrix profile, the state-of-the-art algorithm to perform time series analysis, computes the most similar subsequence for a given query subsequence within a sliced time series. Matrix profile has low arithmetic intensity, but it typically operates on large amounts of time series data. In current computing systems, this data needs to be moved between the off-chip memory units and the on-chip computation units for performing matrix profile. This causes a major performance bottleneck as data movement is extremely costly in terms of both execution time and energy. 
In this work, we present NATSA, the first Near-Data Processing accelerator for time series analysis. The key idea is to exploit modern 3D-stacked High Bandwidth Memory (HBM) to enable efficient and fast specialized matrix profile computation near memory, where time series data resides. NATSA provides three key benefits: 1) quickly computing the matrix profile for a wide range of applications by building specialized energy-efficient floating-point arithmetic processing units close to HBM, 2) improving the energy efficiency and execution time by reducing the need for data movement over slow and energy-hungry buses between the computation units and the memory units, and 3) analyzing time series data at scale by exploiting low-latency, high-bandwidth, and energy-efficient memory access provided by HBM. Our experimental evaluation shows that NATSA improves performance by up to 14.2x (9.9x on average) and reduces energy by up to 27.2x (19.4x on average), over the state-of-the-art multi-core implementation. NATSA also improves performance by 6.3x and reduces energy by 10.2x over a general-purpose NDP platform with 64 in-order cores.

NATSA: A Near-Data Processing Accelerator for Time Series Analysis

Quantum computer simulation using the CUDA programming model

This paper describes how a new Moodle module, called CTPracticals, is applied to the teaching of the practical content of a basic computer organization course. In the core of the module, an automatic verification engine enables it to process the VHDL designs automatically as they are submitted. Moreover, a straightforward modification of this engine would make it possible to extend its application to other programming languages. The module provides students with real-time knowledge of the state of their work by their accessing the result of the automatic assessment or feedback messages. Teachers have a constant global view of the status of their class and have available multiple options such as sending feedback messages to students, obtaining statistics, launching additional verifications in batch, and so on. Likewise, the module substantially improves some organizational aspects, and its design may help teachers to encourage teamwork. Its use partially frees teachers from certain routine work, saving time that can be devoted to teaching objectives and tutoring activities.

Eladio Gutierrez

Papers

A new Moodle module supporting automatic verification of VHDL-based assignments

A compiler method for the parallel execution of irregular reductions in scalable shared memory multiprocessors

NATSA: A Near-Data Processing Accelerator for Time Series Analysis

Quantum computer simulation using the CUDA programming model

Use of a New Moodle Module for Improving the Teaching of a Basic Course on Computer Architecture