# MATLAB 2016a Release Summary for Scientific Computing

#### March 15 2016 in MATLAB, Programming | Tags: 2016a, MATLAB, optimization, parallel | Author: Christopher Rackauckas

There is a lot to read every time MATLAB releases a new version. Here is a summary of what has changed in 2016a from the eyes of someone doing HPC/Scientific Computing/Numerical Analysis. This means I will leave off a lot, and you should check it out yourself but if you're using MATLAB for science then this may cover most of the things you care about.

- Support for sparse matrices on the GPU. A nice addition is sprand and pcg (Preconditioned Conjugate Gradient solvers) for sprase GPU matrices.
- One other big change in the parallel computing toolbox is you can now set nonlinear solvers to estimate gradients and Jacobians in parallel. This should be a nice boost to the MATLAB optimization toolbox.
- In the statistics and machine learning toolbox, they added some algorithms for high dimensional data and now let you run kmeans ... READ MORE

# Interfacing with a Xeon Phi via Julia

#### March 4 2016 in C, HPC, Julia, Programming, Stochastics, Xeon Phi | Tags: C, julia, MIC, OpenMP, parallel, Xeon Phi | Author: Christopher Rackauckas

(Disclaimer: This is not a full-Julia solution for using the Phi, and instead is a tutorial on how to link OpenMP/C code for the Xeon Phi to Julia. There may be a future update where some of these functions are specified in Julia, and Intel's compilertools.jl looks like a viable solution, but for now it's not possible.)

Intel's Xeon Phi has a lot of appeal. It's an instant cluster in your computer, right? It turns out it's not quite that easy. For one, the installation process itself is quite tricky, and the device has stringent requirements for motherboard choices. Also, making out at over a taraflop is good, but not quite as high as NVIDIA's GPU acceleration cards.

However, there are a few big reasons why I think our interest in the Xeon Phi should be renewed. For one, Intel ... READ MORE

# Multiple-GPU Parallelism on the HPC with Julia

#### February 28 2016 in CUDA, HPC, Julia, Programming | Tags: CUDA, gpu, HPC, julia | Author: Christopher Rackauckas

This is the exciting Part 3 to using Julia on an HPC. First I got you started with using Julia on multiple nodes. Second, I showed you how to get the code running on the GPU. That gets you pretty far. However, if you got a trial allocation on Cometand started running jobs, you may have noticed when looking at the architecture that you're not getting to use the full GPU. In the job script I showed you, I asked for 2 GPUs. Why? Well, that's because the flagship NVIDIA GPU, the Tesla K80, is actually a duel GPU and you have to control the two parts separately. You may have been following along on your own computer and have been wondering how you use the multiple GPUs in your setup as well. This tutorial will ... READ MORE

# Tutorial for Julia on the HPC with GPUs

#### February 23 2016 in CUDA, HPC, Julia, Programming | Tags: CUDA, gpu, HPC, julia | Author: Christopher Rackauckas

This is a continuous of my previous post on using Julia on the XSEDE Comet HPC. Check that out first for an explanation of the problem. In that problem, we wished to solve for the area of a region where a polynomial was less than 1, which was calculated by code like: READ MORE

# Multi-node Parallelism in Julia on an HPC (XSEDE Comet)

#### February 23 2016 in HPC, Julia, Programming | Tags: comet, HPC, julia, multi-node, XSEDE | Author: Christopher Rackauckas

Today I am going to show you how to parallelize your Julia code over some standard HPC interfaces. First I will go through the steps of parallelizing a simple code, and then running it with single-node parallelism and multi-node parallelism. The compute resources I will be using are the XSEDE (SDSC) Comet computer (using Slurm) and UC Irvine's HPC (using SGE) to show how to run the code in two of the main job schedulers. You can follow along with the Comet portion by applying and getting a trial allocation. READ MORE

# Blog Upgraded: A Good Experience With Bluehost

#### February 23 2016 in Uncategorized | Tags: | Author: Christopher Rackauckas

My blog is back up and now it's on a more powerful server to deal with the increase in traffic I have been getting. I really want to give a shout out to Bluehost's customer support, I went onto their live chat and talked with someone who really knew the different options. I am going to explain what I learned from Devin and what choice I finally made.

## A Rundown of Bluehost's Hosting Options

Bluehost's hosting options can be summarized as follows:

- Dedicated hosting. If you need this you know what it is.
- VPS. A Virtual Private Server. If you get this kind of server, the server management is up to you. This is the "second most powerful" option (only less than Dedicated), but I did not want to take on the overhead of managing the resource, especially since it would be more than ... READ MORE

# Comparison of US HPC Resources

#### February 20 2016 in HPC | Tags: HPC, XSEDE | Author: Christopher Rackauckas

It can sometimes be quite daunting to get the information you need. When looking for the right HPC to run code on, there are a lot of computers in the US to choose from. I decided to start compiling a lot of the information into some tables in order to make it easier to understand the options. I am right now starting with a small subset which includes Blue Waters and some of XSEDE with rows for the parts that interest me, but if you would like for me to add to the list please let me know. READ MORE

# Using Julia's C Interface to Utilize C Libraries

#### February 4 2016 in C, Julia, Programming | Tags: | Author: Christopher Rackauckas

I recently ran into the problem that Julias's (GNU Scientific Library) GSL.jl library is too new, i.e. some portions don't work as of early 2016. However, to solve my problem I needed access to adaptive Monte Carlo integration methods. This means it was time to go in depth in Julia's C interface. I will go step by step on how I created and compiled the C code, and called it from Julia. READ MORE

# Setting Variables Element-wise in a Matrix in Mathematica

#### February 2 2016 in Mathematica, Programming | Tags: | Author: Christopher Rackauckas

This is the latest entry on tip, tricks, and hacks in Mathematica. In my latest problem I need to solve for the components of many matrices using optimization algorithms. Mathematica does not allow one to straight pass matrices into the algorithms, so one has to perform the algorithms on variables that stand in for the components. This is what I did with the matrix , solving for . That was all fine and dandy until I needed to set some of those matrices to constants. I could do a bunch of set commands all lined up, i.e.

A011 = 2; A021 = 1.4; ...

But that would be inelegant and hard to ... READ MORE

# Simple Parallel Optimization in Mathematica

#### February 2 2016 in Mathematica | Tags: optimization, parallel | Author: Christopher Rackauckas

A quick search on Google did not get hits for a standard method of parallelizing NMaximize and NMinimize, so I wanted to share how I did it.

My implementation is a simple use of a Map-Reduce type of parallelism. What this means is that we map the same problem out to N many processes, and when they finish they each give one result, and we apply a reduction function to reduce the N results to 1. So what we will do is map the NMaximize function to each of N cores, where they will each solve it on a random seed. From there, each process will return what it found as the minimum, and we will take the minimum of these minimums as our best estimate of the global minimums.

Notice that this is not optimal in all cases: for ... READ MORE