You’ve heard that graphics processing units — GPUs — can bring big increases in computational speed.  While GPUs cannot speed up work in every application, the fact is that in many cases it can indeed provide very rapid computation.  In this tutorial, we’ll see how this is done, both in ... [Read more...]

Almost any PC today is multicore.  Dual-core is standard, quad-core is easily attainable for the home, and larger systems, say 16-core, are easily within reach of even smaller research projects. In addition, large multicore systems can be “rented” on Amazon EC2 and so on. The most popular way to program ... [Read more...]

[Read more...]

I’ve now placed the partools package, including Snowdoop, on CRAN.  No major new functions since my last posting, but the existing functions have been made more versatile and convenient, and the documentation is now more detailed, with more examples and so on.  I do have more functions planned. It ... [Read more...]

I’ve put together an updated version of my partools package, including Snowdoop, an alternative to MapReduce algorithms.  You can download it here, version 1.0.1. To review:  The idea of Snowdoop is to create your own file chunking, rather than having something like Hadoop do it for you, and then using ... [Read more...]

In spite of the banter between Yihui and me, I’m glad to hear that he may be interested in Snowdoop, as are some others.  I’m quite busy this week (finishing writing my Parallel Computation for Data Science book, and still have a lot of Fall Quarter grading to ... [Read more...]

I mentioned last week that I would be putting together a package, based in part on my posts on Snowdoop.  I’ve now done so, in a package partools., with the name alluding to the fact that they are intended for use with the cluster-based part of R’s parallel ... [Read more...]

In my last post, I questioned whether the fancy Big Data processing tools such as Hadoop and Spark are really necessary for us R users.  My argument was that (a) these tools tend to be difficult to install and configure, especially for non-geeks; (b) the tools require learning new computation ... [Read more...]

Along with all the hoopla on Big Data in recent years came a lot of hype on Hadoop.  This eventually spread to the R world, with sophisticated packages being developed such as rmr to run on top of Hadoop. Hadoop made it convenient to process data in very large distributed ... [Read more...]

One nice thing about open-source software is that users often have a lot of choices.  Such is the case with R, for instance the thousands of contributed packages available on CRAN.  My focus here is on BLAS, the core of matrix operations in R, where again there are interesting choices ... [Read more...]

In my last post, I agreed with Prof. Xiao-Li Meng that Advanced Placement (AP) Statistics courses turn off many students to the statistics field, by being structured in a manner that makes for a boring class.  I cited as one of the problems the fact that the course officially requires ... [Read more...]

The American Statistical Association (ASA)  leadership, and many in Statistics academia. have been undergoing a period of angst the last few years,  They worry that the field of Statistics is headed for a future of reduced national influence and importance, with the feeling that: The field is to a large ... [Read more...]

Here I will introduce matpow, a package to flexibly and conveniently compute matrix powers.  But even if you are not interested in matrices, I think many of you will find that this post contains much general material on R that you’ll find useful.  Indeed, most of this post will ... [Read more...]

In my JSM talk this morning, I spoke about work done by Yingkang Xie and myself, on a novel approach to the parallel coordinates method of visualization.  I’ve made several posts to this blog in the past on freqparcoord, our implemention of our method. My talk this morning used ... [Read more...]

Last week a reader of the r-help mailing list posted a query titled “Importing random subsets of a data file.”  With a very large file, it is often much easier and faster–and really, just as good–to just work with a much smaller subset of the data. Fellow readers ... [Read more...]

I often create plots that require quite a bit of computation.  Ideally I would run this on what I’ll call Machine A, which is a very fast machine, but I am often far away, on Machine B.  So, I’d like to run my computation on B but display ... [Read more...]

I’ve been mentioning here that I’ll be discussing a new package, Rth, developed by me and Drew Schmidt, the latter of pbdR fame.  It’s now ready for use!  In this post, I’ll explain what goals Rth has, and how to use it. Platform Flexibility The key ... [Read more...]

Before I left for China a few weeks ago, I said my next post would be on our Rth parallel R package. It’s not quite ready yet, so today I’ll post one of the topics I spoke on last night at the Berkeley R Language Beginners Study Group. ... [Read more...]

I’m one of many who bemoan the fact that statistics is typically thought of as — alas, even taught as — a set of formula plugging methods. One enters one’s data, turns the key, and the proper answers pop out. This of course is not the case at all, and ... [Read more...]

The latest issue of the American Statistician has a set of thought-provoking point/counterpoint papers on Simpson’s Paradox, with a tie-in to the controversial issue of causality. (I will not address the causality issue here.) Since I have long had my own thoughts about Simpson’s, I’ll postpone ... [Read more...]