GPUs seem to be all the rage these days. At the last Bayesian Valencia meeting, Chris Holmes gave a nice talk on how GPUs could be leveraged for statistical computing. Recently Christian Robert arXived a paper with parallel computing firmly in mind. In two weeks time I’m giving an internal seminar on using GPUs for statistical computing. To start the talk, I wanted a few graphs that show CPU and GPU evolution over the last decade or so. This turned out to be trickier than I expected.
After spending an afternoon searching the internet (mainly Wikipedia), I came up with a few nice plots.
Intel CPU clock speed
CPU clock speed for a single cpu has been fairly static in the last couple of years – hovering around 3.4Ghz. Of course, we shouldn’t fall completely into the Megahertz myth, but one avenue of speed increase has been blocked:
Computational power per die
Although single CPUs have been limited, due to the rise of multi-core machines, the computational power per die has still been increasing
GPUs vs CPUs
When we compare GPUs with CPUs over the last decade in terms of Floating point operations (FLOPs), we see that GPUs appear to be far ahead of the CPUs
Sources and data
- You can download the data files and R code used to generate the above graphs.
- If you find them useful, please drop me a line.
- I’ll probably write further posts on GPU computing, but these won’t go through the R-bloggers site (since it has little to do with R).
- Data for Figures 1 & 2 was obtained from “Is Parallel Programming Hard, And, If So, What Can You Do About It?” This book got the data from Wikipedia
- Data from Figure 3 was mainly from Wikipedia and the odd mailing list post.
- I believe these graphs show the correct general trend, but the actual numbers have been obtained from mailing lists, Wikipedia, etc. Use with care.
In a recent post, I discussed some statistical consultancy I was involved with. I was quite proud of the nice ggplot2 graphics I had created. The graphs nicely summarised the main points of the paper:
I’ve just had the proofs from the journal, and next to the graphs there is the following note:
It is not usual BJS style to include 95 per cent confidence intervals in K-M
curves. Could you please re-draw Figs 1 & 2 omitting these and INCLUDING ALL
FOUR CURVES IN A SINGLE GRAPH. (If you wish to include 95% c.i., the data could
be produced in tabular form instead.)
They have a policy of not including CI on graphs? So instead of a single nice graphic, they now want a graph and a table with (at least) 9 rows and 5 columns?
Each year I try to carry out some statistical consultancy to give me experience in other areas of statistics and also to provide teaching examples. Last Christmas I was approached by a paediatric consultant from the RVI who wanted to carry out prospective survival analysis. The consultant, Bruce Jaffray, had performed Nissen fundoplication surgery on 230 children. Many of the children had other medical conditions such as cerebral palsy or low BMI. He was interested in the factors that affected patients’ survival.
We fitted a standard cox proportional hazards model. The following covariates were significant:
- cerebral palsy
- need for revision surgery. This was when the child had to return to hospital for more surgery.
- an interaction term between gastrostomy & cerebral palsy.
The interaction term was key to getting a good model fit. The figures (one of which is shown below) were constructed using ggplot2 and R. The referees actually commented on the (good) quality statistical work and nice figures! Always nice to read. Unfortunately, there isn’t a nice survival to ggplot2 interface. I had to write some rather hacky R code 😦
The main finding of the paper was the negative effect of cerebral palsy and gastrostomy on survival. Unfortunately, if a child had a gastronomy or had cerebral palsy then survival was dramatically reduced. The interaction effect was necessary, otherwise we would have predicted that all children with a gastronomy and cerebral palsy wouldn’t survive.
- There was a rather strange and strong gender effect – male survival was greater than female.
- The revision covariate was also significant – children who needed their fundoplication redone had increased survival. At first glance this is strange – the operation had to be redone, yet this was good for survival. However, this was really a red herring. Essentially children who had their surgery redone had by definition survived a minimum amount of time. I think something a bit more sophisticated could have been done with this variable, but the numbers weren’t that large.