Graphical processing units (GPUs) are all the rage these days. Most journal issues would be incomplete if at least one article didn’t mention the word “GPUs”. Like any good geek, I was initially interested with the idea of using GPUs for statistical computing. However, last summer I messed about with GPUs and the sparkle was removed. After looking at a number of papers, it strikes me that reviewers are forgetting to ask basic questions when reviewing GPU papers.

- For speed comparisons, do the authors compare a GPU with a
**multi-core**CPU. In many papers, the comparison is with a single-core CPU. If a programmer can use CUDA, they can certainly code in pthreads or openMP. Take off a factor of eight when comparing to a multi-core CPU. - Since a GPU has (usually) been bought specifically for the purpose of the article, the CPU can be a few years older. So, take off a factor of two for each year of difference between a CPU and GPU.
- I like programming with doubles. I don’t really want to think about single precision and all the difficulties that entails. However, many CUDA programs are compiled as single precision. Take off a factor of two for double precision.
- When you use a GPU, you split the job in blocks of threads. The number of threads in each block depends on the type of problem under consideration and can have a massive speed impact on your problem. If your problem is something like matrix multiplication, where each thread multiplies two elements, then after a few test runs, it’s straightforward to come up with an optimal thread/block ratio. However, if each thread is a stochastic simulation, it now becomes very problem dependent. What could work for one model, could well be disastrous for another.

So in many GPU articles the speed comparisons could be reduced by a factor of 32!

Just to clarify, I’m not saying that GPUs have no future, rather, there has been some mis-selling of their potential usefulness in the (statistical) literature.