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CE_27C Essentials of High Performance and Parallel Statistical Computing with RINSTRUCTOR(S): Wei-Chen Chen and George OstrouchovThis is an introductory course in high performance and parallel statistical computing, which is essential for statistical modeling when dealing with big data. We introduce fundamentals of parallel statistical computing including the use of the pbdR package ecosystem on larger platforms. We present a broad overview of parallel programming paradigms and relate parallel approaches within R for statistical computation. Practical examples beginning with strategies for speeding up serial R code and continuing with parallel approaches of increasing complexity are discussed. Computing platforms ranging from multicore laptops to medium and even large distributed systems are covered. We bring a coherent approach that is based on established advanced parallel computing concepts from the high performance computing (HPC) community, all within the comfort of R. Basic knowledge of R and statistical computing are assumed.
Sugawara, Tanaka, Okazaki, Watanabe, and Sasato (2012) examined the effects of praise on motor learning using a serial finger-tapping task. The results indicated that the group that was praised for their performance had significantly better performance than did either the group that watched other participants receive praise or the group that was not praised when the participants suddenly performed the learned serial finger-tapping movement. However, there were no significant differences among the three groups when the participants performed the task using a new serial random-order movement. They thus concluded that motor skill improvements due to praise are observed when praise is provided while the task is not being performed (off-line monitoring) rather than during the task (on-line monitoring). 1e1e36bf2d