Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones

The growth in the use of computationally intensive statistical procedures, especially with big data, has necessitated the usage of parallel computation on diverse platforms such as multicore, GPUs, clusters and clouds. However, slowdown due to interprocess communication costs typically limits such methods to "embarrassingly parallel" (EP) algorithms, especially on non-shared memory platforms. This paper develops a broadlyapplicable method for converting many non-EP algorithms into statistically equivalent EP ones. The method is shown to yield excellent levels of speedup for a variety of statistical computations. It also overcomes certain problems of memory limitations

On the Need for Reform of the H-1B Non-Immigrant Work Visa in Computer-Related Occupations

The H-1B program authorizes non-immigrant visas under which skilled foreign workers may be employed in the U.S., typically in computer-related positions. Congress greatly expanded the program in 1998 and then again in 2000, in response to heavy pressure from industry, which claimed a desperate software labor shortage. After presenting an overview of the H-1B program in Parts II and III, the Article will show in Part IV that these shortage claims are not supported by the data. Part V will then show that the industry\u27s motivation for hiring H-lBs is primarily a desire for cheap, compliant labor. The Article then discusses the adverse impacts of the H-1B program on various segments of the American computer-related labor force in Part VI, and presents proposals for reforms in Part VII

The R Student Companion

Abstracts not available for BookReview