Big data is the current hype, the thing you need to do to find the best job in the world. I’ve started using machine learning tools a decade ago, and when I saw this book, it felt like it was answering some concerns I had. Let’s see what’s inside.
Last year, my colleagues and I presented a paper on giga model simulations in an SPE conference: Giga-Model Simulations In A Commercial Simulator – Challenges & Solutions. During this talk, we talked about the complexity of I/O for such simulations. We had ordered data as input that we needed to split in chunks to send them on the relevant MPI ranks, and then the same process was required for writing the results, gathering the chunks and then writing them down to the disk.
The central point is that some clusters have parallel file systems, and these works well when you try to access big blobs of aligned data. In fact, as they are the bottleneck of the whole system, you need to limit the number of accesses to what you actually require. For instance in HDF5, you can specify the alignment of datasets, so you can say that all HDF5 datasets will be aligned on the filesystem specifications (so for instance 1MB if your Lustre/GPFS has a chunk size of 1MB) and read or write chunks that are multiple of these values.
Sometimes, it’s so easy to rewrite some existing code because it doesn’t fit exactly your bill.
I just so the example with an All To All communication that was written by hand. The goal was to share how many elements would be sent from one MPI process to another, and these elements were stored on one process in different structure instances, one for each MPI process. So in the end, you had n structures on each of the n MPI processes.
The MPI_Alltoall cannot map directly to this scattered structure, so it sounds fair to assume that using MPI_Isend and MPI_Irecv would be simpler to implement. The issue is that this pattern uses buffers on each process for each other process it will send values to or receive values from. A lot of MPI library allocate their buffer when needed, but will never let go of the memory until the end. So you end up with a memory consumption that doesn’t scale. In my case, when using more than 1000 cores, the MPI library uses more than 1GB per MPI process when it hits these calls, just for these additional hidden buffers. This is just no manageable.
Now, if you use MPI_Alltoall, two things happen:
there are no additional buffer allocated, so this scales nicely when you increase the number of cores
it is actually faster than your custom implementation
Now with MPI 3 standard having non-blocking collective operations, there is absolutely no reason to try to outsmart the library when you need a collective operation. It has heuristics when it knows that it is doing a collective call, so let them work. You won’t be smarter if you try, but you will if you use them.
In my case, the code to retrieve all values and store them in an intermediate buffer was smaller that the one with the Isend/Irecv.
In the new C++ standard, multithread finally appears, with the old standard supported with TR2. This new addition has numerous implications on how programs are coded, and there are of course almost no book on this matter. This one is an exception.
We know now that we won’t have the same serial computing increase we had in the last decades. We have to cope with optimizing serial codes, and programming parallel and concurrent ones, and this means that all coders have to cope with this paradigm shift. If computer scientists are aware of the tools to use, it is not the same for the “average” scientist or engineer. And this is the purpose of this book: educate the average coder. Continue reading Book review: Introduction to High Performance Computing for Scientists and Engineers→