## Archive for the ‘parallel programming’ Category

I work at The University of Sheffield where I am one of the leaders of the new Research Software Engineering function. One of the things that my group does is help people make use of Sheffield’s High Performance Computing cluster, Iceberg.

Iceberg is a heterogenous system with around 3440 CPU cores and a sprinkling of GPUs. It’s been in use for several years and has been upgraded a few times over that period. It’s a very traditional HPC system that makes use of Linux and a variant of Sun Grid Engine as the scheduler and had served us well.

A while ago, the sysadmin pointed me to a goldmine of a resource — Iceberg’s accounting log. This 15 Gigabyte file contains information on every job submitted since July 2009. That’s more than 7 years of the HPC usage of 3249 users — over 46 million individual jobs.

The file format is very straightforward. There’s one line per job and each line consists of a set of colon separated fields. The first few fields look like something like this:

long.q:node54.iceberg.shef.ac.uk:el:abc07de:

The username is field 4 and the number of slots used by the job is field 35. On our system, slots correspond to CPU cores. If you want to run a 16 core job, you ask for 16 slots.

With one line of awk, we can determine the maximum number of slots ever requested by each user.

gawk -F: '$35>=slots[$4] {slots[$4]=$35};END{for(n in slots){print n, slots[n]}}' accounting > ./users_max_slots.csv

As a quick check, I grepped the output file for my username and saw that the maximum number of cores I’d ever requested was 20. I ran a 32 core MPI ‘Hello World’ job, reran the line of awk and confirmed that my new maximum was 32 cores.

There are several ways I could have filtered the number of users but I was having awk lessons from David Jones so let’s create a new file containing the users who have only ever requested 1 slot.

gawk -F: '$35>=slots[$4] {slots[$4]=$35};END{for(n in slots){if(slots[n]==1){print n, slots[n]}}}' accounting > users_where_max_is_one_slot.csv

Running wc on these files allows us to determine how many users are in each group

wc users_max_slots.csv 3250 6498 32706 users_max_slots.csv

One of those users turned out to be a blank line so 3249 usernames have been used on Iceberg over the last 7 years.

wc users_where_max_is_one_slot.csv 2393 4786 23837 users_where_max_is_one_slot.csv

That is, 2393 of our 3249 users (just over 73%) over the last 7 years have only ever run 1 slot, and therefore 1 core, jobs.

**High Performance?**

So **73% of all users have only ever submitted single core jobs**. This does not necessarily mean that they have not been making use of parallelism. For example, they might have been running job arrays – hundreds or thousands of single core jobs performing parameter sweeps or monte carlo simulations.

Maybe they **were** running parallel codes but only asked the scheduler for one core. In the early days this would have led to oversubscribed nodes, possibly up to 16 jobs, each trying to run 16 cores.These days, our sysadmin does some voodoo to ensure that jobs can only use the number of cores that have been requested, no matter how many threads their code is spawning. Either way, making this mistake is not great for performance.

Whatever is going on, this figure of 73% is surprising to me!

Thanks to David Jones for the awk lessons although if I’ve made a mistake, it’s all my fault!

**Update (11th Jan 2017)**

UCL’s Ian Kirker took a look at the usage of their general purpose cluster and found that 71.8% of their users have only ever run 1 core jobs. https://twitter.com/ikirker/status/819133966292807680

I was sat in a meeting recently where people were discussing High Performance Computing options. Someone had found some money down the back of the sofa and asked the rest of us how we’d spend it if it were ours (For the record, I wanted some big-memory nodes — 512Gb RAM minimum).

Halfway through the meeting someone asked about the parallel filesystem in use on Sheffield’s HPC systems and I answered Lustre — not expecting any further comment. I don’t know much about parallel I/O systems but I do know that all of the systems I have access to make use of Lustre. Sheffield’s Iceberg, Manchester’s Computational Shared Facility and the UK National supercomputer, Archer to name three. The Wikipedia page for Lustre suggests that it is very widely used in the HPC community worldwide.

I was surprised, then, when this comment was met with derision from some in the room. One person remarked ‘It’s a bit…um…classic..isn’t it?’, giving the impression that it was old and outdated. There was not much love for dear old Lustre it seemed!

There was no time in this meeting for me ask ‘What’s wrong with it then? Works fine for me!’so I did what I often do in times like this, I asked twitter:

Q for HPC followers. Is there anything wrong with using Lustre? Just been in a meeting where it was mocked as ‘classic’.

— Mike Croucher (@walkingrandomly) April 12, 2016

I found the responses to be very instructive so thought I would share them here. Here’s a comment from Ben Waugh

@walkingrandomly Lustre failures seem to be the main cause of downtime in my limited experience of large HPC systems.

— Ben Waugh (@benwaughuk) April 12, 2016

Not a great start! I’ve been hit by Lustre downtime too but, since I’m not a sysadmin, I haven’t got a genuine sense of how big a problem it is. It happens often enough that I’m aware of it but not so often that I feel the need to worry about it. Looking after large HPC systems is difficult and when one attempts to do do something difficult, the occasional failure is inevitable.

It seems that configuration might play a part in stability and performance. Peter Van Heusden kicked off with

@walkingrandomly Depending on the configuration can lack resilience, but is still standard HPC parallel FS.

— Peter van Heusden (@pvanheus) April 12, 2016

Adrian Jackson , Research Architect, HPC and Parallel Programmer, is thinking along similar lines.

@walkingrandomly 1/2 Yes, for a long time the metadata servers have not kept up, meaning you can destroy it with lots of small files

— Adrian Jackson (@adrianjhpc) April 12, 2016

@walkingrandomly 2/2 Although I believe this is being fixed in a variety of ways.

— Adrian Jackson (@adrianjhpc) April 12, 2016

@walkingrandomly Also, the default settings often don’t give best large scale performance, but that’s a matter of configuration

— Adrian Jackson (@adrianjhpc) April 12, 2016

Adrian also followed up with an interesting report on Performance of Parallel IO on ARCHER from June 2015. Thanks Adrian!

Glen K Lockwood is a Computational scientist specialising in I/O platforms at @NERSC, the flagship computing centre for the U.S. Dept. of Energy’s Office of Science. Glen has this insight,

@eoinbrazil @walkingrandomly Lustre just gets trashed because everyone uses it (because of its cost). Parallel file systems all misbehave

— Glenn K. Lockwood (@glennklockwood) April 12, 2016

I like this comment because it fits with my own, much less experienced, view of the parallel I/O world. A classic demonstration of confirmation bias perhaps but I see this thought pattern in a lot of areas. The pattern looks like this:

It’s hard to do ‘thing’. Most smart people do ‘thing’ with ‘foo’ and, since ‘thing’ is hard, many people have experienced problems with ‘foo’. Hence, people bash ‘foo’ a lot. ‘foo’ sucks!

Alternatives exist of course but there may be trade-offs. Here’s Mark Basham, software scientist ~~@~~**diamondlightsou** in the UK.

@walkingrandomly we use it at Diamond for several of our file systems. GPFS is a bit better, but a lot more costly.

— Mark Basham (@basham_mark) April 12, 2016

I’ll give the final word to Nick Holway who sums up how I feel about parallel storage from a user’s point of view.

@walkingrandomly I like my HPC storage to be "classic" and also rather "boring"

— Nick Holway (@nickholway) April 12, 2016

Intel have finally released the Xeon Phi – an accelerator card based on 60 or so customised Intel cores to give around a Teraflop of double precision performance. That’s comparable to the latest cards from NVIDIA (1.3 Teraflops according to http://www.theregister.co.uk/2012/11/12/nvidia_tesla_k20_k20x_gpu_coprocessors/) but with one key difference—you don’t need to learn any new languages or technologies to take advantage of it (although you can do so if you wish)!

The Xeon Phi uses good, old fashioned High Performance Computing technologies that we’ve been using for years such as OpenMP and MPI. There’s no need to completely recode your algorithms in CUDA or OpenCL to get a performance boost…just a sprinkling of OpenMP pragmas might be enough in many cases. Obviously it will take quite a bit of work to squeeze every last drop of performance out of the thing but this might just be the realisation of ‘personal supercomputer’ we’ve all been waiting for.

Here are some links I’ve found so far — would love to see what everyone else has come up with. I’ll update as I find more

- http://www.theregister.co.uk/2012/11/12/intel_xeon_phi_coprocessor_launch/ (Includes pricing and some benchmarks)
- http://www.streamcomputing.eu/blog/2012-11-12/intels-answer-to-amd-and-nvidia-the-xeon-phi-5110p/ (lots of details, programming models and comparisons with GPUs)
- http://software.intel.com/en-us/blogs/2012/11/12/introducing-opencl-12-for-intel-xeon-phi-coprocessor (Intel’s OpenCL works on Xeon Phi)
- http://www.hpcwire.com/hpcwire/2012-11-12/nag_delivers_numerical_software_to_xeon_phi.html (My favourite numerical library has already been ported to the Phi)

I also note that the Xeon Phi uses AVX extensions but with a wider vector width of 512 bytes so if you’ve been taking advantage of that technology in your code (using one of these techniques perhaps) you’ll reap the benefits there too.

I, for one, am very excited and can’t wait to get my hands on one! Thoughts, comments and links gratefully received!

**Updated 26th March 2015**

I’ve been playing with AVX vectorisation on modern CPUs off and on for a while now and thought that I’d write up a little of what I’ve discovered. The basic idea of vectorisation is that **each processor core** in a modern CPU can operate on multiple values (i.e. a vector) simultaneously per instruction cycle.

Modern processors have 256bit wide vector units which means that each CORE can perform up to **16 double precision or 32 single precision floating point operations (FLOPS) per clock cycle**. So, on a quad core CPU that’s typically found in a decent laptop you have 4 vector units (one per core) and could perform up to **64 double precision FLOPS per cycle**. The Intel Xeon Phi accelerator unit has even wider vector units — 512bit!

This all sounds great but how does a programmer actually make use of this neat hardware trick? There are many routes:-

**Intrinsics**

At the ‘close to the metal’ level you code for these vector units using instructions called AVX intrinsics. This is relatively difficult and leads to none-portable code if you are not careful.

- The Intel Intrinsics Guide – An interactive reference tool for Intel intrinsic instructions,
- Introduction to Intel Advanced Vector Extensions – includes some example C++ programs using AVX intinsics
- Benefits of Intel AVX for small matrices – More code examples along with speed comparisons.

**Auto-vectorisation in compilers**

Since working with intrinsics is such hard work, why not let the compiler take the strain? Many modern compilers can automatically vectorize your C, C++ or Fortran code including gcc, PGI and Intel. Sometimes all you need to do is add an extra switch at compile time and reap the speed benefits. In truth, vectorization isn’t always automatic and the programmer needs to give the compiler some assistance but it is a lot easier than hand-coding intrinsics.

- A Guide to Auto-vectorization with Intel C++ Compilers – Exactly what it says. In my experience, the intel compilers do auto-vectorisation better than other compilers.
- Auto-vectorisation in gcc 4.7 – A superb article showing how auto-vectorisation works in practice when using gcc 4.7. Lots of C code examples along with the emitted assembler and a good discussion of the hints you may need to give to the compiler to get maximum performance.
- Auto-vectorisation in gcc – The project page for auto-vectorisation in gcc
- Optimizing Application Performance on x64 Processor-based Systems with PGI Compilers and Tools – Includes discussion and example of auto-vectorisation using the PGI compiler
- Jim Radigan: Inside Auto-Vectorization, 1 of n – A video by a Microsoft engineer working on Visual Studio 2012. A superb introduction to what vectorisation is along with speed-up demonstrations and discussion on how the auto-vectoriser will work in Visual Studio 2012.
- Auto Vectorizer in Visual Studio 2012 – A series of blog articles about vectorization in Visual Studio 2012.

**Intel SPMD Program Compiler (ispc)**

There is a midway point between automagic vectorisation and having to use intrinsics. Intel have a free compiler called ispc (http://ispc.github.com/) that allows you to write compute kernels in a modified subset of C. These kernels are then compiled to make use of vectorised instruction sets. Programming using ispc feels a little like using OpenCL or CUDA. I figured out how to hook it up to MATLAB a few months ago and developed a version of the Square Root function that is almost twice as fast as MATLAB’s own version for sandy bridge i7 processors.

- http://ispc.github.com/ – The website for ispc
- http://ispc.github.com/perf.html – Some performance metrics. In some cases combining vectorisation and parallelisation can increase single precision throughput by more than a factor of 32 on a quad-core machine!
*ispc: A SPMD Compiler For High-Performance CPU Programming*, Illinois-Intel Parallelism Center Distinguished Speaker Series (UIUC), March 15, 2012. (talk video–requires Windows Media Player.) This link was taken from Matt Pharr’s website (The author of ispc).

**OpenMP**

OpenMP is an API specification for parallel programming that’s been supported by several compilers for many years. OpenMP 4 was released in mid 2013 and included support for vectorisation.

- Performance Essentials with OpenMP 4.0 Vectorization – A series of seven videos from Intel covering performance essentials using OpenMP 4.0 Vectorization with C/C++.

- Explicit Vector Programming with OpenMP 4.0 SIMD Extensions – A tutorial from HPC today.

**Vectorised Libraries**

Vendors of numerical libraries are steadily applying vectorisation techniques in order to maximise performance. If the execution speed of your application depends upon these library functions, you may get a significant speed boost simply by updating to the latest version of the library and recompiling with the relevant compiler flags.

- Yeppp! – A fast, vectorised math library (benchmarks here)
- NAG Library for Xeon Phi – A huge, commercial library for the Intel Xeon Phi Accelerator
- Intel AVX optimization in Intel Math Kernel Library (MKL) – See what’s been vectorised in version 10.3 of the MKL
- Intel Integrated Performance Primitives (IPP) Functions Optimized for AVX – The IPP library includes many basic algorithms used in image and signal processing
- SIMD Library for Evaluating Elementary Functions (SLEEF) – An open-source, vectorised library for the calculation of various mathematical functions. Someone has done benchmarks for it here.
- SIMD-oriented Fast Mersenne Twister (SFMT) – Uses vectorisation to implement a very fast random number generation.

**CUDA for x86**

Another route to vectorised code is to make use of the PGI Compiler’s support for x86 CUDA. What you do is take CUDA kernels written for NVIDIA GPUs and use the PGI Compiler to compile these kernels for x86 processors. The resulting executables take advantage of vectorisation. In essence, the vector units of the CPU are acting like CUDA cores–which they sort of are anyway!

The PGI compilers also have technology which they call PGI Unified binary which allows you to use NVIDIA GPUs when present or default to using multi-core x86 if no GPU is present.

- PGI CUDA-x86 – PGI’s main page for their CUDA on x86 technologies

**OpenCL for x86 processors**

Yet another route to vectorisation would be to use Intel’s OpenCL implementation which takes OpenCL kernels and compiles them down to take advantage of vector units (http://software.intel.com/en-us/blogs/2011/09/26/autovectorization-in-intel-opencl-sdk-15/). The AMD OpenCL implementation may also do this but I haven’t tried it and haven’t had chance to research it yet.

**WalkingRandomly posts**

I’ve written a couple of blog posts that made use of this technology.

- Using Intel’s SPMD Compiler (ispc) with MATLAB on Linux
- Using the Portland PGI Compiler for MATLAB mex files in Windows

**Miscellaneous resources**

There is other stuff out there but the above covers everything that I have used so far. I’ll finish by saying that everyone interested in vectorisation should check out this website…It’s the bible!

**Research Articles on SSE/AVX vectorisation
**

I found the following research articles useful/interesting. I’ll add to this list over time as I dig out other articles.

Intel have just released their OpenCL Software Development Kit (SDK) for Intel processors. The good news is that this version targets the on-die GPU as well as the CPU allowing truly heterogeneous programming. The bad news is that the GPU goodness is for 3rd Generation ‘Ivy Bridge‘ Processors only– us backward Sandy Bridge users have been left in the cold :(

A quick scan through the release notes reveals the following:-

- OpenCL access to the on-die GPU part is currently for Windows only. Linux users only have CPU support at the moment.
- No access to the GPU part of Sandy Bridge Processors via this implementation.
- The GPU part has single precision only (I guess we’ll see many more mixed-precision algorithms from now on)

I don’t have access to an Ivy Bridge processor and so can’t have a play but I’m looking forward to seeing how much performance OpenCL programmers can squeeze out of this new implementation.

**Other WalkingRandomly posts on GPU computing**

Modern CPUs are capable of parallel processing at multiple levels with the most obvious being the fact that a typical CPU contains multiple processor cores. My laptop, for example, contains a quad-core Intel Sandy Bridge i7 processor and so has 4 processor cores. You may be forgiven for thinking that, with 4 cores, my laptop can do up to 4 things simultaneously but life isn’t quite that simple.

The first complication is hyper-threading where each physical core appears to the operating system as two or more virtual cores. For example, the processor in my laptop is capable of using hyper-threading and so I have access to up to 8 virtual cores! I have heard stories where unscrupulous sales people have passed off a 4 core CPU with hyperthreading as being as good as an 8 core CPU…. after all, if you fire up the Windows Task Manager you can see 8 cores and so there you have it! However, this is very far from the truth since what you really have is 4 real cores with 4 brain damaged cousins. Sometimes the brain damaged cousins can do something useful but they are no substitute for physical cores. There is a great explanation of this technology at makeuseof.com.

The second complication is the fact that **each physical processor core** contains a SIMD (Single Instruction Multiple Data) lane of a certain width. SIMD lanes, aka SIMD units or vector units, can process several numbers simultaneously with a single instruction rather than only one a time. The 256-bit wide SIMD lanes on my laptop’s processor, for example, can operate on up to 8 single (or 4 double) precision numbers per instruction. Since each physical core has its own SIMD lane this means that a 4 core processor could theoretically operate on up to 32 single precision (or 16 double precision) numbers per clock cycle!

So, all we need now is a way of programming for these SIMD lanes!

Intel’s SPMD Program Compiler, ispc, is a free product that allows programmers to take direct advantage of the SIMD lanes in modern CPUS using a C-like syntax. The speed-ups compared to single-threaded code can be impressive with Intel reporting up to 32 times speed-up (on an i7 quad-core) for a **single precision** Black-Scholes option pricing routine for example.

**Using ispc on MATLAB
**

Since ispc routines are callable from C, it stands to reason that we’ll be able to call them from MATLAB using mex. To demonstrate this, I thought that I’d write a sqrt function that works faster than MATLAB’s built-in version. This is a tall order since the sqrt function is pretty fast and is already multi-threaded. Taking the square root of 200 million random numbers doesn’t take very long in MATLAB:

>> x=rand(1,200000000)*10; >> tic;y=sqrt(x);toc Elapsed time is 0.666847 seconds.

This might not be the most useful example in the world but I wanted to focus on how to get ispc to work from within MATLAB rather than worrying about the details of a more interesting example.

**Step 1 – A reference single-threaded mex file**

Before getting all fancy, let’s write a nice, straightforward single-threaded mex file in C and see how fast that goes.

#include <math.h> #include "mex.h" void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { double *in,*out; int rows,cols,num_elements,i; /*Get pointers to input matrix*/ in = mxGetPr(prhs[0]); /*Get rows and columns of input matrix*/ rows = mxGetM(prhs[0]); cols = mxGetN(prhs[0]); num_elements = rows*cols; /* Create output matrix */ plhs[0] = mxCreateDoubleMatrix(rows, cols, mxREAL); /* Assign pointer to the output */ out = mxGetPr(plhs[0]); for(i=0; i<num_elements; i++) { out[i] = sqrt(in[i]); } }

Save the above to a text file called** sqrt_mex.c** and compile using the following command in MATLAB

mex sqrt_mex.c

Let’s check out its speed:

>> x=rand(1,200000000)*10; >> tic;y=sqrt_mex(x);toc Elapsed time is 1.993684 seconds.

Well, it works but it’s quite a but slower than the built-in MATLAB function so we still have some work to do.

**Step 2 – Using the SIMD lane on one core via ispc**

Using ispc is a two step process. First of all you need the .ispc program

export void ispc_sqrt(uniform double vin[], uniform double vout[], uniform int count) { foreach (index = 0 ... count) { vout[index] = sqrt(vin[index]); } }

Save this to a file called** ispc_sqrt.ispc** and compile it at the **Bash prompt** using

ispc -O2 ispc_sqrt.ispc -o ispc_sqrt.o -h ispc_sqrt.h --pic

This creates an object file, **ispc_sqrt.o**, and a header file, **ispc_sqrt.h**. Now create the mex file in MATLAB

#include <math.h> #include "mex.h" #include "ispc_sqrt.h" void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { double *in,*out; int rows,cols,num_elements,i; /*Get pointers to input matrix*/ in = mxGetPr(prhs[0]); /*Get rows and columns of input matrix*/ rows = mxGetM(prhs[0]); cols = mxGetN(prhs[0]); num_elements = rows*cols; /* Create output matrix */ plhs[0] = mxCreateDoubleMatrix(rows, cols, mxREAL); /* Assign pointer to the output */ out = mxGetPr(plhs[0]); ispc::ispc_sqrt(in,out,num_elements); }

Call this **ispc_sqrt_mex.cpp ** and compile **in MATLAB** with the command

mex ispc_sqrt_mex.cpp ispc_sqrt.o

Let’s see how that does for speed:

>> tic;y=ispc_sqrt_mex(x);toc Elapsed time is 1.379214 seconds.

So, we’ve improved on the single-threaded mex file a bit (1.37 instead of 2 seconds) but it’s still not enough to beat the MATLAB built-in. To do that, we are going to have to use the SIMD lanes on all 4 cores simultaneously.

**Step 3 – A reference multi-threaded mex file using OpenMP**

Let’s step away from ispc for a while and see how we do with something we’ve seen before– a mex file using OpenMP (see here and here for previous articles on this topic).

#include <math.h> #include "mex.h" #include <omp.h> void do_calculation(double in[],double out[],int num_elements) { int i; #pragma omp parallel for shared(in,out,num_elements) for(i=0; i<num_elements; i++){ out[i] = sqrt(in[i]); } } void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { double *in,*out; int rows,cols,num_elements,i; /*Get pointers to input matrix*/ in = mxGetPr(prhs[0]); /*Get rows and columns of input matrix*/ rows = mxGetM(prhs[0]); cols = mxGetN(prhs[0]); num_elements = rows*cols; /* Create output matrix */ plhs[0] = mxCreateDoubleMatrix(rows, cols, mxREAL); /* Assign pointer to the output */ out = mxGetPr(plhs[0]); do_calculation(in,out,num_elements); }

Save this to a text file called **openmp_sqrt_mex.c** and compile **in MATLAB** by doing

mex openmp_sqrt_mex.c CFLAGS="\$CFLAGS -fopenmp" LDFLAGS="\$LDFLAGS -fopenmp"

Let’s see how that does (OMP_NUM_THREADS has been set to 4):

>> tic;y=openmp_sqrt_mex(x);toc Elapsed time is 0.641203 seconds.

That’s very similar to the MATLAB built-in and I suspect that The Mathworks have implemented their sqrt function in a very similar manner. Theirs will have error checking, complex number handling and what-not but it probably comes down to a for-loop that’s been parallelized using Open-MP.

**Step 4 – Using the SIMD lanes on all cores via ispc**

To get a ispc program to run on all of my processors cores simultaneously, I need to break the calculation down into a series of tasks. The .ispc file is as follows

task void ispc_sqrt_block(uniform double vin[], uniform double vout[], uniform int block_size,uniform int num_elems){ uniform int index_start = taskIndex * block_size; uniform int index_end = min((taskIndex+1) * block_size, (unsigned int)num_elems); foreach (yi = index_start ... index_end) { vout[yi] = sqrt(vin[yi]); } } export void ispc_sqrt_task(uniform double vin[], uniform double vout[], uniform int block_size,uniform int num_elems,uniform int num_tasks) { launch[num_tasks] < ispc_sqrt_block(vin, vout, block_size, num_elems) >; }

Compile this by doing the following at the **Bash prompt**

ispc -O2 ispc_sqrt_task.ispc -o ispc_sqrt_task.o -h ispc_sqrt_task.h --pic

We’ll need to make use of a task scheduling system. The ispc documentation suggests that you could use the scheduler in Intel’s Threading Building Blocks or Microsoft’s Concurrency Runtime but a basic scheduler is provided with ispc in the form of tasksys.cpp (I’ve also included it in the .tar.gz file in the downloads section at the end of this post), We’ll need to compile this too so do the following **at the Bash prompt**

g++ tasksys.cpp -O3 -Wall -m64 -c -o tasksys.o -fPIC

Finally, we write the mex file

#include <math.h> #include "mex.h" #include "ispc_sqrt_task.h" void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { double *in,*out; int rows,cols,i; unsigned int num_elements; unsigned int block_size; unsigned int num_tasks; /*Get pointers to input matrix*/ in = mxGetPr(prhs[0]); /*Get rows and columns of input matrix*/ rows = mxGetM(prhs[0]); cols = mxGetN(prhs[0]); num_elements = rows*cols; /* Create output matrix */ plhs[0] = mxCreateDoubleMatrix(rows, cols, mxREAL); /* Assign pointer to the output */ out = mxGetPr(plhs[0]); block_size = 1000000; num_tasks = num_elements/block_size; ispc::ispc_sqrt_task(in,out,block_size,num_elements,num_tasks); }

In the above, the input array is divided into tasks where each task takes care of 1 million elements. Our 200 million element test array will, therefore, be split into 200 tasks– many more than I have processor cores. I’ll let the task scheduler worry about how to schedule these tasks efficiently across the cores in my machine. Compile this **in MATLAB** by doing

mex ispc_sqrt_task_mex.cpp ispc_sqrt_task.o tasksys.o

Now for crunch time:

>> x=rand(1,200000000)*10; >> tic;ys=sqrt(x);toc %MATLAB's built-in Elapsed time is 0.670766 seconds. >> tic;y=ispc_sqrt_task_mex(x);toc %my version using ispc Elapsed time is 0.393870 seconds.

There we have it! A version of the sqrt function that works faster than MATLAB’s own by virtue of the fact that I am now making full use of the SIMD lanes in my laptop’s Sandy Bridge i7 processor thanks to ispc.

Although this example isn’t very useful as it stands, I hope that it shows that using the ispc compiler from within MATLAB isn’t as hard as you might think and is yet another tool in the arsenal of weaponry that can be used to make MATLAB faster.

**Final Timings, downloads and links
**

- Single threaded: 2.01 seconds
- Single threaded with ispc: 1.37 seconds
- MATLAB built-in: 0.67 seconds
- Multi-threaded with OpenMP (OMP_NUM_THREADS=4): 0.64 seconds
- Multi-threaded with OpenMP and hyper-threading (OMP_NUM_THREADS=8): 0.55 seconds
- Task-based multicore with ispc: 0.39 seconds

Finally, here’s some links and downloads

**System Specs**

- MATLAB 2011b running on 64 bit linux
- gcc 4.6.1
- ispc version 1.1.1
- Intel Core i7-2630QM with 8Gb RAM

The NAG C Library is one of the largest commercial collections of numerical software currently available and I often find it very useful when writing MATLAB mex files. “*Why is that?*” I hear you ask.

One of the main reasons for writing a mex file is to gain more speed over native MATLAB. However, one of the main problems with writing mex files is that you have to do it in a low level language such as Fortran or C and so you lose much of the ease of use of MATLAB.

In particular, you lose straightforward access to most of the massive collections of MATLAB routines that you take for granted. Technically speaking that’s a lie because you could use the mex function **mexCallMATLAB** to call a MATLAB routine from within your mex file but then you’ll be paying a time overhead every time you go in and out of the mex interface. Since you are going down the mex route in order to gain speed, this doesn’t seem like the best idea in the world. This is also the reason why you’d use the NAG C Library and not the NAG Toolbox for MATLAB when writing mex functions.

One way out that I use often is to take advantage of the NAG C library and it turns out that it is extremely easy to add the NAG C library to your mex projects on Windows. Let’s look at a trivial example. The following code, nag_normcdf.c, uses the NAG function **nag_cumul_normal **to produce a simplified version of MATLAB’s normcdf function (laziness is all that prevented me from implementing a full replacement).

/* A simplified version of normcdf that uses the NAG C library * Written to demonstrate how to compile MATLAB mex files that use the NAG C Library * Only returns a normcdf where mu=0 and sigma=1 * October 2011 Michael Croucher * www.walkingrandomly.com */ #include <math.h> #include "mex.h" #include "nag.h" #include "nags.h" void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { double *in,*out; int rows,cols,num_elements,i; if(nrhs>1) { mexErrMsgIdAndTxt("NAG:BadArgs","This simplified version of normcdf only takes 1 input argument"); } /*Get pointers to input matrix*/ in = mxGetPr(prhs[0]); /*Get rows and columns of input matrix*/ rows = mxGetM(prhs[0]); cols = mxGetN(prhs[0]); num_elements = rows*cols; /* Create output matrix */ plhs[0] = mxCreateDoubleMatrix(rows, cols, mxREAL); /* Assign pointer to the output */ out = mxGetPr(plhs[0]); for(i=0; i<num_elements; i++){ out[i] = nag_cumul_normal(in[i]); } }

To compile this in MATLAB, just use the following command

mex nag_normcdf.c CLW6I09DA_nag.lib

If your system is set up the same as mine then the above should ‘just work’ (see **System Information** at the bottom of this post). The new function works just as you would expect it to

>> format long >> format compact >> nag_normcdf(1) ans = 0.841344746068543

Compare the result to normcdf from the statistics toolbox

>> normcdf(1) ans = 0.841344746068543

So far so good. I could stop the post here since all I really wanted to do was say **‘The NAG C library is useful for MATLAB mex functions and it’s a doddle to use – here’s a toy example and here’s the mex command to compile it’**

However, out of curiosity, I looked to see if my toy version of normcdf was any faster than The Mathworks’ version. Let there be 10 million numbers:

>> x=rand(1,10000000);

Let’s time how long it takes MATLAB to take the normcdf of those numbers

>> tic;y=normcdf(x);toc Elapsed time is 0.445883 seconds. >> tic;y=normcdf(x);toc Elapsed time is 0.405764 seconds. >> tic;y=normcdf(x);toc Elapsed time is 0.366708 seconds. >> tic;y=normcdf(x);toc Elapsed time is 0.409375 seconds.

Now let’s look at my toy-version that uses NAG.

>> tic;y=nag_normcdf(x);toc Elapsed time is 0.544642 seconds. >> tic;y=nag_normcdf(x);toc Elapsed time is 0.556883 seconds. >> tic;y=nag_normcdf(x);toc Elapsed time is 0.553920 seconds. >> tic;y=nag_normcdf(x);toc Elapsed time is 0.540510 seconds.

So my version is slower! Never mind, I’ll just make my version parallel using OpenMP – Here is the code: nag_normcdf_openmp.c

/* A simplified version of normcdf that uses the NAG C library * Written to demonstrate how to compile MATLAB mex files that use the NAG C Library * Only returns a normcdf where mu=0 and sigma=1 * October 2011 Michael Croucher * www.walkingrandomly.com */ #include <math.h> #include "mex.h" #include "nag.h" #include "nags.h" #include <omp.h> void do_calculation(double in[],double out[],int num_elements) { int i,tid; #pragma omp parallel for shared(in,out,num_elements) private(i,tid) for(i=0; i<num_elements; i++){ out[i] = nag_cumul_normal(in[i]); } } void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) { double *in,*out; int rows,cols,num_elements; if(nrhs>1) { mexErrMsgIdAndTxt("NAG_NORMCDF:BadArgs","This simplified version of normcdf only takes 1 input argument"); } /*Get pointers to input matrix*/ in = mxGetPr(prhs[0]); /*Get rows and columns of input matrix*/ rows = mxGetM(prhs[0]); cols = mxGetN(prhs[0]); num_elements = rows*cols; /* Create output matrix */ plhs[0] = mxCreateDoubleMatrix(rows, cols, mxREAL); /* Assign pointer to the output */ out = mxGetPr(plhs[0]); do_calculation(in,out,num_elements); }

Compile that with

mex COMPFLAGS="$COMPFLAGS /openmp" nag_normcdf_openmp.c CLW6I09DA_nag.lib

and on my quad-core machine I get the following timings

>> tic;y=nag_normcdf_openmp(x);toc Elapsed time is 0.237925 seconds. >> tic;y=nag_normcdf_openmp(x);toc Elapsed time is 0.197531 seconds. >> tic;y=nag_normcdf_openmp(x);toc Elapsed time is 0.206511 seconds. >> tic;y=nag_normcdf_openmp(x);toc Elapsed time is 0.211416 seconds.

This is faster than MATLAB and so normal service is resumed :)

**System Information**

- 64bit Windows 7
- MATLAB 2011b
- NAG C Library Mark 9 – CLW6I09DAL
- Visual Studio 2008
- Intel Core i7-2630QM processor

So, you’re the proud owner of a new license for MATLAB’s parallel computing toolbox (PCT) and you are wondering how to get some bang for your buck as quickly as possible. Sure, you are going to learn about constructs such as parfor and spmd but that takes time and effort. Wouldn’t it be nice if you could speed up some of your MATLAB code simply by saying **‘Turn parallelisation on’**?

It turns out that The Mathworks have been adding support for their parallel computing toolbox all over the place and all you have to do is switch it on (Assuming that you actually have the parallel computing toolbox of course). For example say you had the following call to fmincon (part of the optimisation toolbox) in your code

[x, fval] = fmincon(@objfun, x0, [], [], [], [], [], [], @confun,opts)

To turn on parallelisation across 2 cores just do

matlabpool 2; opts = optimset('fmincon'); opts = optimset('UseParallel','always'); [x, fval] = fmincon(@objfun, x0, [], [], [], [], [], [], @confun,opts);

That wasn’t so hard was it? The speedup (if any) completely depends upon your particular optimization problem.

**Why isn’t parallelisation turned on by default?**

The next question that might occur to you is ** ‘Why doesn’t The Mathworks just turn parallelisation on by default?’** After all, although the above modification is straightforward, it does require you to know that this particular function supports parallel execution via the PCT. If you didn’t think to check then your code would be doomed to serial execution forever.

The simple answer to this question is ‘**Sometimes the parallel version is slower**‘. Take this serial code for example.

objfun = @(x)exp(x(1))*(4*x(1)^2+2*x(2)^2+4*x(1)*x(2)+2*x(2)+1); confun = @(x) deal( [1.5+x(1)*x(2)-x(1)-x(2); -x(1)*x(2)-10], [] ); tic; [x, fval] = fmincon(objfun, x0, [], [], [], [], [], [], confun); toc

On the machine I am currently sat at (quad core running MATLAB 2011a on Linux) this typically takes around 0.032 seconds to solve. With a problem that trivial my gut feeling is that we are not going to get much out of switching to parallel mode.

objfun = @(x)exp(x(1))*(4*x(1)^2+2*x(2)^2+4*x(1)*x(2)+2*x(2)+1); confun = @(x) deal( [1.5+x(1)*x(2)-x(1)-x(2); -x(1)*x(2)-10],[] ); %only do this next line once. It opens two MATLAB workers matlabpool 2; opts = optimset('fmincon'); opts = optimset('UseParallel','always'); tic; [x, fval] = fmincon(objfun, x0, [], [], [], [], [], [], confun,opts); toc

Sure enough, this increases execution time dramatically to an average of 0.23 seconds on my machine. There is always a computational overhead that needs paying when you go parallel and if your problem is too trivial then this overhead costs more than the calculation itself.

**So which functions support the Parallel Computing Toolbox?**

I wanted a web-page that listed** all** functions that gain benefit from the Parallel Computing Toolbox but couldn’t find one. I found some documentation on specific toolboxes such as Parallel Statistics but nothing that covered all of MATLAB in one place. Here is my attempt at producing such a document. Feel free to contact me if I have missed anything out.

This covers MATLAB 2011b and is almost certainly incomplete. I’ve only covered toolboxes that I have access to and so some are missing. Please contact me if you have any extra information.

**Bioinformatics Toolbox**

**Global Optimisation**

- Various solvers use the PCT. See this part of the MATLAB documentation for details.

**Image Processing
**

- blockproc
- Note that many Image Processing functions run in parallel even without the parallel computing toolbox. See my article Which MATLAB functions are Multicore Aware?

**Optimisation Toolbox**

**Simulink**

- Running parallel simulations
- You can increase the speed of diagram updates for models containing large model reference hierarchies by building referenced models that are configured in Accelerator mode in parallel whenever conditions allow. This is covered in the documentation.

**Statistics Toolbox**

- bootstrp
- bootci
- cordexch
- candexch
- crossval
- dcovary
- daugment
- growTrees
- jackknife
- lasso
- nnmf
- plsregress
- rowexch
- sequentialfs
- TreeBagger

**Other articles about parallel computing in MATLAB from WalkingRandomly**

- Which MATLAB functions are multicore aware? There are a ton of functions in MATLAB that take advantage of parallel processors automatically. No Parallel Computing Toolbox necessary.
- Parallel MATLAB with OpenMP mex files Want to parallelize your own functions without purchasing the PCT? Not afraid to get your hands dirty with C? Perhaps this option is for you.
- MATLAB GPU/CUDA Experiences and tutorials on my laptop – A series of articles where I look a GPU computing with CUDA on MATLAB

This is part 1 of an ongoing series of articles about MATLAB programming for GPUs using the Parallel Computing Toolbox. The introduction and index to the series is at http://www.walkingrandomly.com/?p=3730.

Have you ever needed to take the sine of 100 million random numbers? Me either, but such an operation gives us an excuse to look at the basic concepts of GPU computing with MATLAB and get an idea of the timings we can expect for simple elementwise calculations.

**Taking the sine of 100 million numbers on the CPU**

Let’s forget about GPUs for a second and look at how this would be done on the CPU using MATLAB. First, I create 100 million random numbers over a range from 0 to 10*pi and store them in the variable cpu_x;

cpu_x = rand(1,100000000)*10*pi;

Now I take the sine of all 100 million elements of cpu_x using a single command.

cpu_y = sin(cpu_x)

I have to confess that I find the above command very cool. Not only are we looping over a massive array using just a single line of code but MATLAB will also be performing the operation** in parallel**. So, if you have a multicore machine (and pretty much everyone does these days) then the above command will make good use of many of those cores. Furthermore, this kind of parallelisation is built into the core of MATLAB….no parallel computing toolbox necessary. As an aside, if you’d like to see a list of functions that automatically run in parallel on the CPU then check out my blog post on the issue.

So, how quickly does my 4 core laptop get through this 100 million element array? We can find out using the MATLAB functions **tic** and **toc**. I ran it three times on my laptop and got the following

>> tic;cpu_y = sin(cpu_x);toc Elapsed time is 0.833626 seconds. >> tic;cpu_y = sin(cpu_x);toc Elapsed time is 0.899769 seconds. >> tic;cpu_y = sin(cpu_x);toc Elapsed time is 0.916969 seconds.

So the first thing you’ll notice is that the timings vary and I’m not going to go into the reasons why here. What I am going to say is that because of this variation it makes sense to time the calculation a number of times (20 say) and take an average. Let’s do that

sintimes=zeros(1,20); for i=1:20;tic;cpu_y = sin(cpu_x);sintimes(i)=toc;end average_time = sum(sintimes)/20 average_time = 0.8011

So, on average, it takes my quad core laptop just over 0.8 seconds to take the sine of 100 million elements using the CPU. A couple of points:

- I note that this time is smaller than any of the three test times I did before running the loop and I’m not really sure why. I’m guessing that it takes my CPU a short while to decide that it’s got a lot of work to do and ramp up to full speed but further insights are welcomed.
- While staring at the CPU monitor I noticed that the above calculation never used more than 50% of the available virtual cores. It’s using all 4 of my physical CPU cores but perhaps if it took advantage of hyperthreading I’d get even better performance? Changing OMP_NUM_THREADS to 8 before launching MATLAB did nothing to change this.

**Taking the sine of 100 million numbers on the GPU**

Just like before, we start off by using the CPU to generate the 100 million random numbers^{1}

cpu_x = rand(1,100000000)*10*pi;

The first thing you need to know about GPUs is that they have their own memory that is completely separate from main memory. So, the GPU doesn’t know anything about the array created above. Before our GPU can get to work on our data we have to transfer it from main memory to GPU memory and we acheive this using the **gpuArray** command.

gpu_x = gpuArray(cpu_x); %this moves our data to the GPU

Once the GPU can see all our data we can apply the sine function to it very easily.

gpu_y = sin(gpu_x)

Finally, we transfer the results back to main memory.

cpu_y = gather(gpu_y)

If, like many of the GPU articles you see in the literature, you don’t want to include transfer times between GPU and host then you time the calculation like this:

tic gpu_y = sin(gpu_x); toc

Just like the CPU version, I repeated this calculation several times and took an average. The result was 0.3008 seconds giving a** speedup of 2.75 times compared to the CPU version**.

If, however, you include the time taken to transfer the input data to the GPU and the results back to the CPU then you need to time as follows

tic gpu_x = gpuArray(cpu_x); gpu_y = sin(gpu_x); cpu_y = gather(gpu_y) toc

On my system this takes 1.0159 seconds on average– **longer than it takes to simply do the whole thing on the CPU**. So, for this particular calculation, transfer times between host and GPU swamp the benefits gained by all of those CUDA cores.

**Benchmark code**

I took the ideas above and wrote a simple benchmark program called sine_test. The way you call it is as follows

[cpu,gpu_notransfer,gpu_withtransfer] = sin_test(numrepeats,num_elements]

For example, if you wanted to run the benchmarks 20 times on a 1 million element array and return the average times then you just do

>> [cpu,gpu_notransfer,gpu_withtransfer] = sine_test(20,1e6) cpu = 0.0085 gpu_notransfer = 0.0022 gpu_withtransfer = 0.0116

I then ran this on my laptop for array sizes ranging from 1 million to 100 million and used the results to plot the graph below.

**But I wanna write a ‘GPUs are awesome’ paper**

So far in this little story things are not looking so hot for the GPU and yet all of the **‘GPUs are awesome’ **papers you’ve ever read seem to disagree with me entirely. What on earth is going on? Well, lets take the advice given by csgillespie.wordpress.com and turn it on its head. How do we get awesome speedup figures from the above benchmarks to help us pump out a ‘GPUs are awesome paper’?

0. Don’t consider transfer times between CPU and GPU.

We’ve already seen that this can ruin performance so let’s not do it shall we? As long as we explicitly say that we are not including transfer times then we are covered.

1. Use a singlethreaded CPU.

Many papers in the literature compare the GPU version with a single-threaded CPU version and yet I’ve been using all 4 cores of my processor. Silly me…let’s fix that by running MATLAB in single threaded mode by launching it with the command

matlab -singleCompThread

Now when I run the benchmark for 100 million elements I get the following times

>> [cpu,gpu_no,gpu_with] = sine_test(10,1e8) cpu = 2.8875 gpu_no = 0.3016 gpu_with = 1.0205

Now we’re talking! I can now claim that my GPU version is over 9 times faster than the CPU version.

2. Use an old CPU.

My laptop has got one of those new-fangled sandy-bridge i7 processors…one of the best classes of CPU you can get for a laptop. If, however, I was doing these tests at work then I guess I’d be using a GPU mounted in my university Desktop machine. Obviously I would compare the GPU version of my program with the CPU in the Desktop….an Intel Core 2 Quad Q9650. Heck its running at 3Ghz which is more Ghz than my laptop so to the casual observer (or a phb) it would look like I was using a more beefed up processor in order to make my comparison fairer.

So, I ran the CPU benchmark on that (in singleCompThread mode obviously) and got 4.009 seconds…noticeably slower than my laptop. Awesome…I am definitely going to use that figure!

I know what you’re thinking…Mike’s being a fool for the sake of it but csgillespie.wordpress.com puts it like this ** ‘Since a GPU has (usually) been bought specifically for the purpose of the article, the CPU can be a few years older.’ **So, some of those ‘GPU are awesome’ articles will be accidentally misleading us in exactly this manner.

3. Work in single precision.

Yeah I know that you like working with double precision arithmetic but that slows GPUs down. So, let’s switch to single precision. Just argue in your paper that single precision is OK for this particular calculation and we’ll be set. To change the benchmarking code all you need to do is change every instance of

rand(1,num_elems)*10*pi;

to

rand(1,num_elems,'single')*10*pi;

Since we are reputable researchers we will, of course, modify both the CPU and GPU versions to work in single precision. Timings are below

- Desktop at work (single thread, single precision): 3.49 seconds
- Laptop GPU (single precision, not including transfer): 0.122 seconds

OK, so switching to single precision made the CPU version a bit faster but it’s more than doubled GPU performance. We can now say that the GPU version is over 28 times faster than the CPU version. Now we have ourselves a bone-fide ‘GPUs are awesome’ paper.

4. Use the best GPU we can find

So far I have been comparing the CPU against the relatively lowly GPU in my laptop. Obviously, however, if I were to do this for real then I’d get a top of the range Tesla. It turns out that I know someone who has a Tesla C2050 and so we ran the single precision benchmark on that. The result was astonishing…0.0295 seconds for 100 million numbers not including transfer times. The double precision performance for the same calculation on the C2050 was 0.0524 seonds.

5. Write the abstract for our ‘GPUs are awesome’ paper

*We took an Nvidia Tesla C2050 GPU and mounted it in a machine containing an Intel Quad Core CPU running at 3Ghz. We developed a program that performs element-wise trigonometry on arrays of up to 100 million single precision random numbers using both the CPU and the GPU. The GPU version of our code ran up to 118 times faster than the CPU version. GPUs are awesome!*

** **

**Results from different CPUs and GPUs. Double precision, multi-threaded**

I ran the sine_test benchmark on several different systems for 100 million elements. The CPU was set to be multi-threaded and double precision was used throughout.

sine_test(10,1e8)

GPUs

- Tesla C2050, Linux, 2011a – 0.7487 seconds including transfers, 0.0524 seconds excluding transfers.
- GT 555M – 144 CUDA Cores, 3Gb RAM, Windows 7, 2011a (My laptop’s GPU) -1.0205 seconds including transfers, 0.3016 seconds excluding transfers

CPUs

- Intel Core i7-880 @3.07Ghz, Linux, 2011a – 0.659 seconds
- Intel Core i7-2630QM, Windows 7, 2011a (My laptop’s CPU) – 0.801 seconds
- Intel Core 2 Quad Q9650 @ 3.00GHz, Linux – 0.958 seconds

**Conclusions**

- MATLAB’s new GPU functions are very easy to use! No need to learn low-level CUDA programming.
- It’s very easy to massage CPU vs GPU numbers to look impressive. Read those ‘GPUs are awesome’ papers with care!
- In real life you have to consider data transfer times between GPU and CPU since these can dominate overall wall clock time with simple calculations such as those considered here. The more work you can do on the GPU, the better.
- My laptop’s GPU is nowhere near as good as I would have liked it to be. Almost 6 times slower than a Tesla C2050 (excluding data transfer) for elementwise double precision calculations. Data transfer times seem to about the same though.

**Next time**

In the next article in the series I’ll look at an element-wise calculation that really is worth doing on the GPU – even using the wimpy GPU in my laptop – and introduce the MATLAB function arrayfun.

**Footnote**

1 – MATLAB 2011a can’t create random numbers directly on the GPU. I have no doubt that we’ll be able to do this in future versions of MATLAB which will change the nature of this particular calculation somewhat. Then it will make sense to include the random number generation in the overall benchmark; transfer times **to** the GPU will be non-existant. In general, however, we’ll still come across plenty of situations where we’ll have a huge array in main memory that needs to be transferred to the GPU for further processing so what we learn here will not be wasted.

**Hardware / Software used for the majority of this article**

- Laptop model: Dell XPS L702X
- CPU: Intel Core i7-2630QM @2Ghz software overclockable to 2.9Ghz. 4 physical cores but total 8 virtual cores due to Hyperthreading.
- GPU: GeForce GT 555M with 144 CUDA Cores. Graphics clock: 590Mhz. Processor Clock:1180 Mhz. 3072 Mb DDR3 Memeory
- RAM: 8 Gb
- OS: Windows 7 Home Premium 64 bit. I’m not using Linux because of the lack of official support for Optimus.
- MATLAB: 2011a with the parallel computing toolbox

**Other GPU articles at Walking Randomly**

- GPU Support in Mathematica, Maple, MATLAB and Maple Prime – See the various options available
- Insert new laptop to continue – My first attempt at using the GPU functionality in MATLAB
- NVIDIA lets down Linux laptop users – and how an open source project saves the day

Thanks to various people at The Mathworks for some useful discussions, advice and tutorials while creating this series of articles.

These days it seems that you can’t talk about scientific computing for more than 5 minutes without somone bringing up the topic of Graphics Processing Units (GPUs). Originally designed to make computer games look pretty, GPUs are massively parallel processors that promise to revolutionise the way we compute.

A brief glance at the specification of a typical laptop suggests why GPUs are the new hotness in numerical computing. Take my new one for instance, a Dell XPS L702X, which comes with a Quad-Core Intel i7 Sandybridge processor running at up to 2.9Ghz and an NVidia GT 555M with a whopping 144 CUDA cores. If you went back in time a few years and told a younger version of me that I’d soon own a 148 core laptop then young Mike would be stunned. He’d also be wondering ‘What’s the catch?’

Of course the main catch is that all processor cores are not created equally. Those 144 cores in my GPU are, individually, rather wimpy when compared to the ones in the Intel CPU. It’s the sheer quantity of them that makes the difference. The question at the forefront of my mind when I received my shiny new laptop was ‘Just how much of a difference?’

Now I’ve seen lots of articles that compare CPUs with GPUs and the GPUs always win…..by a lot! Dig down into the meat of these articles, however, and it turns out that things are not as simple as they seem. Roughly speaking, the abstract of some them could be summed up as ‘** We took a serial algorithm written by a chimpanzee for an old, outdated CPU and spent 6 months parallelising and fine tuning it for a top of the line GPU. Our GPU version is up to 150 times faster!**‘

Well it would be wouldn’t it?! In other news, Lewis Hamilton can drive his F1 supercar around Silverstone faster than my dad can in his clapped out 12 year old van! These articles are so prevalent that csgillespie.wordpress.com recently published an excellent article that summarised everything you should consider when evaluating them. What you do is take the claimed speed-up, apply a set of common sense questions and thus determine a realistic speedup. That factor of 150 can end up more like a factor of 8 once you think about it the right way.

That’s not to say that GPUs aren’t powerful or useful…it’s just that maybe they’ve been hyped up a bit too much!

So anyway, back to my laptop. It doesn’t have a top of the range GPU custom built for scientific computing, instead it has what Notebookcheck.net refers to as a * fast middle class graphics card for laptops*. It’s got all of the required bits though….144 cores and CUDA compute level 2.1 so surely it can whip the built in CPU even if it’s just by a little bit?

I decided to find out with a few randomly chosen tests. I wasn’t aiming for the kind of rigor that would lead to a peer reviewed journal but I did want to follow some basic rules at least

- I will only choose algorithms that have been optimised and parallelised for both the CPU and the GPU.
- I will release the source code of the tests so that they can be critised and repeated by others.
- I’ll do the whole thing in MATLAB using the new GPU functionality in the parallel computing toolbox. So, to repeat my calculations all you need to do is copy and paste some code. Using MATLAB also ensures that I’m using good quality code for both CPU and GPU.

**The articles
**

This is the introduction to a set of articles about GPU computing on MATLAB using the parallel computing toolbox. Links to the rest of them are below and more will be added in the future.

- Elementwise operations on the GPU #1 – Basic commands using the PCT and how to write a ‘GPUs are awesome’ paper; no matter what results you get!
- Elementwise operations on the GPU #2 – A slightly more involved example showing a useful speed-up compared to the CPU. An introduction to MATLAB’s arrayfun
- Optimising a correlated asset calculation on MATLAB #1: Vectorisation on the CPU – A detailed look at a port from CPU MATLAB code to GPU MATLAB code.
- Optimising a correlated asset calculation on MATLAB #2: Using the GPU via the PCT – A detailed look at a port from CPU MATLAB code to GPU MATLAB code.
- Optimising a correlated asset calculation on MATLAB #3: Using the GPU via Jacket – A detailed look at a port from CPU MATLAB code to GPU MATLAB code.

**External links of interest to MATLABers with an interest in GPUs**

- The Parallel Computing Toolbox (PCT) – The Mathwork’s MATLAB add-on that gives you CUDA GPU support.
- Mike Gile’s MATLAB GPU Blog – from the University of Oxford
- Accelereyes – Developers of ‘Jacket’, an alternative to the parallel computing toolbox.
- A Mandelbrot Set on the GPU – Using the parallel computing toolbox to make pretty pictures…FAST!
- GP-you.org – A free CUDA-based GPU toolbox for MATLAB
- Matlab, CUDA and Me – Stu Blair gives various examples of calling CUDA kernels directly from MATLAB