OpenCL on the Parallella - Structure, Communication and Debugging for Noobs

In this post, I'll cover the basic program structure of a simple OpenCL program, inter-core communication, dynamic space allocation and debugging when there is no debugger.

I'm writing on a Parallella-16 (server version) using the Brown Deer stdcl library (version 1.6) which is using the eSDK (version 5.13.09.10) from the 14.04 Ubuntu build. My example code can be cloned from github with the command:

git clone -b blog9Files https://github.com/nickoppen/nnP.git

Programming in OpenCL

The first thing to remember about OpenCL was that it was devised to provide a "standard" language to off load numerically intensive tasks from the CPU to a GPU. The Epiphany accelerator is similar to a GPU in that it is a remote device, with no operating system and memory is not shared with the CPU.

This means that you have to transmit the program and data to the accelerator, execute the program and retrieve the results. Once the program has halted, you have to start the whole process again. The data and code you had there previously will have disappeared.

Also, there are no basic operating system functions like threads or interrupts. You can program them yourself if you want but you have to start from scratch (or use something like freeRTOS). This is what's known as "bare metal" programming.

So, if this is what you are up for, let's start.

Basic Structure

If you've read my post on NDRanges will be familiar with the calls clndrange_init1D and forka. These are a short hand way of distributing a kernel to a specific number of cores where you want those cores to carry out exactly the same task on data you supply. This is the classic Single Instruction Multiple Data (SIMD) execution plan that suits some problems very well. Matrix multiplication which lies at the heart of the feed-forward back-propagation neural network is one such problem.

The following diagram and code illustrates how this works for the Parallella.

In the above diagram, I've included all the calls you need to get your program and data to and from the accelerator. Here are the critical bits on the host side:

• You must use the OpenCL types for all variables that you will pass. Here I've use cl_T to indicate OpenCL types. They start with cl_ followed by one of the basic types (int, float etc.). This will ensure that the types that you use on the host side will be the same width as the accelerator.
• Pointer variables (e.g. arrays) must be allocated to an OpenCL context (the standard context for the accelerator is stdacc) using clmalloc. Note that argument 2 is the size of the space allocated in bytes, so multiply your array size by the size of the contained type.
• Use the clopen command that suits your chosen compilation strategy. The first one is for kernels that have been linked at compile time (see my previous blog post on Code::Blocks). The second is for the JIT compiler with the cl code in a file and the third (clsopen) is for the JIT compiler with the kernel in a string (for more discussion see the section on Space Allocation below).
• The clmsync using the CL_MEM_DEVICE indicates that the data is outbound from the host to the accelerator. CL_MEM_HOST indicates inbound data.
• For a discussion of cl_ndrange and forka see my previous blog post titled "What was that NDRange thing?". Note that all arguments are passed by value, therefore it is not necessary to call clmsync for simple variable types like cl_int and cl_float.
• All calls that enqueue an activity to the accelerator have the flag CL_EVENT_NO_WAIT. This indicates that the host should execute the command and continue without waiting for the accelerator to complete the task. The cl_wait command ensures that they are all finished before continuing. (The cl_flush is there because the example code from Brown Deer uses one. The clmesg that appears during execution indicates that cl_flush has not been implemented. I think it is supposed to complete all "enqueuements".)

On the Epiphany side you are (almost) back in the wonderful world of C-language programming. I say almost because there are a few additional things you have to think about and a few restrictions (especially if you have no debugger).

The arguments to the kernel are passed by value therefore, the arrays (arr_in, and arr_out) are pointer into the Epiphany's global memory (I'm not sure where this memory actually is but it is remote from the core). I think that it is better to treat these arguments like Unix named pipes rather than variables. You can use them directly but the communication overhead in doing so is going to be large. Therefore, I declared local arrays and copied the remote data to them before starting the computation. Once this is complete, the results are copied back to remote memory before the kernel exits.

Note that I have used a simple for loop to copy the data from global to local memory. The examples supplied by Brown Deer all use the memcopy extension. I have not been able to find out how OpenCL extensions are used on the Parallella and so I've opted for a simple alternative that works in the same way which I'll replace as soon as I'm able.

Note also that the local arrays are declared statically with a fixed size at compile time. There was a post on the forums where djm asked about dynamic allocation and the response from dar (David Ritchie at Brown Deer) was not very promising (see: here). Therefore I've opted again for a simple, obvious approach using static arrays the size of which I manipulate on the fly (see Space Allocation below). 

Once you are in your kernel, the need to put cl_ on the front of variables disappears. I've put the symbol __private in front of the arrays but I think that is redundant. With no memory equivalent to OpenCL __local memory (memory shared between groups of cores), anything declared within the kernel is automatically private.

Space Allocation

Static array sizes are a problem if you have to decide how large they are before you know what data they will have to accommodate. If you use the offline compiler (as described in my Code::Blocks post) this is what you have to do. However, with a Just In Time compiler you can calculate the size required and generate code (or compiler directives) when the host code is running and alter the kernel source code before it is compiled.

Calling the JIT compiler can be done in two ways. The simpler way is using the call:

void * handle = clopen(stdacc, strKernelSourceFile, CLLD_NOW);

This reads the source file named in argument 2 and compiles it straight away. The source file is just like any other.

You can also use:

void * handle = clsopen(stdacc, strKernelCode, CLLD_NOW);

In this call, argument 2 is a string containing the actual code. The advantage of this call is that there are no files to be read and your kernel code is compiled into your executable. It is still not exactly secure (anyone can use a text editor to open the executable file and look for readable text) but at least you don't need to deliver two files when you deploy your system. The downside is that the code needs new line characters to make it humanly readable. You can deliver your kernel in one enormous line and the compiler will not complain but that is not really practical for humans. To use a new line character in a string you need to use the \ character at the end of each line. I decided to use the first method just to avoid having to maintain the \ characters. 

I think the solution is to develop using the first strategy and then write a quick pre-processing step that generates the string from the source code for Release version. This would either add the \ before the new line character or strip out the new line characters all together (along with the // comments that need a new line to terminate).

Example

In my feed forward example I use a dynamically create include file filled with #defined variables to contain all characteristics of the neural network that do not change. To generate them I open, write to and close a file called cldefs.inc.

At the top of the file:
#define PATHTOCLDEFSFILE "//home//linaro//Work//nnP//cldefs.inc"

Inside the class, I calculate the values I want to define (see the function setNetworkTopology) and then call:

void writeDefsFile()
{
    fstream * pFile;
    cl_int i;

    if (checkExists(PATHTOCLDEFSFILE, true))
    {
        pFile = new fstream();
        pFile->open(PATHTOCLDEFSFILE, ios::out);
  (*pFile) << "#define CORECOUNT 16\n";
  (*pFile) << "#define LAYERCOUNT " << layerCount << "\n#define OUTPUTLAYER " << (layerCount - 1) << "\n";
  (*pFile) << "#define MAXWEIGHTTOLAYER " << maxWeightToLayer << "\n";
  (*pFile) << "#define LARGESTDERIVEDLAYER " << largestDerivedLayer << "\n";
  (*pFile) << "#define LARGESTINPUTLAYER " << largestInputLayer << "\n";
  (*pFile) << "#define INITWIDTHARRAY {";
for (i=0; i<layerCount-1; i++)
            (*pFile) << (*layers)[i].nodeCount << ",";
        (*pFile) << (*layers)[i].nodeCount << "}\n";
  pFile->close();
  delete pFile;
    }
    else
  throw format_Error(ENN_ERR_CL_DEFS_NOT_FOUND);
}

Which produces the file:

#define CORECOUNT 16

#define LAYERCOUNT 4

#define OUTPUTLAYER 3

#define MAXWEIGHTTOLAYER 493

#define LARGESTDERIVEDLAYER 29

#define LARGESTINPUTLAYER 29

#define INITWIDTHARRAY {8,17,29,12}

And finally, in the cl file the #include and an example to remind me:

#include "/home/linaro/Work/nnP/cldefs.inc"

/// cldefs.inc contains #defines for all static variables

/// example contents of cldefs.inc

///#define CORECOUNT 16

///#define LAYERCOUNT 4

///#define OUTPUTLAYER 3          

///#define MAXWEIGHTTOLAYER 1024

///#define LARGESTDERIVEDLAYER 32

///#define LARGESTINPUTLAYER 32   

///#define INITWIDTHARRAY {32,32,16,16}/

And later the declarations:

    /// local storage

    __private int   widths[] = INITWIDTHARRAY;

    __private float wgt[MAXWEIGHTTOLAYER];

    __private float biases[LARGESTDERIVEDLAYER];

    __private float in[LARGESTINPUTLAYER];

    __private float derived[LARGESTDERIVEDLAYER];

Works a treat!

Intercore Communication

If you've read my very first post on the design of the neural network simulator, you will have seen that each intermediate value calculated by one core needs to be distributed to all of the other cores for use in the next round. To do this I use this little gem which was brought to my attention by djm:

#define LOCAL_MEM_ADDRESS_BASE(gid) (((32 + ((gid) / 4)) << 26) | ((8 + ((gid) % 4)) << 20))

This calculates the memory base address of a core given the global id (int gid = get_global_id(0)). It is defined as a macro to avoid the overhead of a function call.

To use this you then need to provide the offset of the structure into which you want to write your value:

*(float *)(LOCAL_MEM_ADDRESS_BASE(gid_next) + ((unsigned int) derived) + (index * sizeof(float))) = value_being_passed;

The components of this snippet are:

• LOCAL_MEM_ADDRESS_BASE calculates the beginning of the memory range
• ((unsigned int) derived) is the address (pointer to) the derived value array which is being cast to an unsigned int for the addition
• (index * sizeof(float)) is the index of the array into which we want to write the value_being_passed. Don't forget, this is all byte arithmetic so you need to multiply the index by the size of the contain type to land up in the right place.

One BIG note here: this will only work if the code on the core that is being written to is the same as the core doing the writing. The location of the remote array is based on the address on the local core. If the core that is being written to has allocated the array in a different place you'll be writing somewhere you don't expect. 

Example

Here is an example from my feed forward kernel:

gid_next = (gid == (CORECOUNT - 1)) ? 0 : gid + 1;

while (gid_next != gid)

{

    for (n=localFirstNode; n < localLastNode; n++)

       *(float *)(LOCAL_MEM_ADDRESS_BASE(gid_next) + ((unsigned int) derived) + (n*sizeof(float))) = derived[n];

    gid_next = (gid_next == CORECOUNT - 1) ? 0 : gid_next + 1;

    barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE);

//  debug - watch the derived values arriving at core 0 from the other nodes

//  if (gid == 0)

//      for (i=0; i<curLayerWidth; i++)

//          debug[d++] = derived[i];

}

Each core iterates through the values it has derived and writes them into the derived array of each other core.

Note the barrier at the end of each iteration through the nodes. This is to keep all cores in sync while they go about messing with each other memory. This is handy during debugging but barriers should be used sparingly given they stall all cores until they all have reached the barrier. This ensures that the slowest core determines the execution time of all cores up to that point in time. In this case I don't believe that they are necessary because I don't need anything else to be ready before I do the next write. I'll check that out when I focus on performance rather than correctness.

I've left some debugging code in this snippet. I'll cover that below.

A Few Notes About Performance and Efficiency

While I have not spent too much time of performance or efficiency there were a few things that occurred to me while I was writing this kernel.  

One thing that was obvious enough to be put in from the beginning. I pass all the weights needed to calculate the output for the whole network into one big array called weights. This array dominates the space requirement for the calculation. For this reason, I only copy in the weights needed to calculate the nodes assigned to each core into a local array called wgt. In this way not only is the calculate spread out over all cores but the space requirement is as well. (NB now that I come to write this I notice that I've actually allocated enough space on each core for all the weights between the biggest two layers - 16 times as much as they actually need. I'll fix that up in the next version).

There is also something that I can improve on the host side. The structure that I've presented above is that the host (ARM cores) provide the input data to the accelerator, fires off the kernel and then waits til it is done. With neural networks and many other applications, the kernel is called multiple times with new data. Therefore, it would be more efficient for the host to leave the accelerator running while it collects the next input set. Then, after the next input set is ready to be sent, would wait. Thus, the execution time would be the slower of the host or the accelerator rather than the addition of the two.

The inter-core communication method that I've described above also leave a lot to be desired. Each core writes to each other core's memory regardless of how far it is away in the mesh. By my calculation, if each core wrote one value to each other core, that would generate 640 inter-core write operations. It's hard to determine how long this would take given that they all happen in parallel and there would be clashes, but I'm sure there is a better way. Two possibilities come to mind:

• Use the base address for the core to determine where it is in the mesh and then only write to it's nearest neighbour in a predefined topology, starting with the value the core had calculated itself and then passing on values that it had received from it's neighbours.
• Use the Epiphany's broadcast mechanism.

I've got no idea how to do either of these just yet so they will have to be the subject of another post.

Debugging

I mentioned in my last post that I did not have any luck getting the Epiphany debugger going with OpenCL code. This is still the case.

Because I was mainly working with floats, I declared an array of floats on the host and made it 2048 items wide. I passed this to the kernel and gave myself an index int d = 0;. As the kernel executed I wrote values directly into the __global array, incrementing d on each assignment. I could write from one core as in the debug example above. Or, if I wanted for example 18 values from each core, I reinitialised d as 18 times the global id and each core wrote to different parts of the array. Then, on the host side, I pulled them back just like my output values and wrote them to standard output separated by commas. I then copied the output to a file and loaded it into a spreadsheet as a csv file. I had to then make sense out of it with a lot of copying and pasting but at least I could see what was going on. Primitive, but the best I came up with and it did get the job done. I've left a few examples in the source on github.

The process I used was:

• Check the input using the debugger on the host side. If you don't start from a known position you'll have no hope of understanding what's going on in the kernel. 
• Check the input on the accelerator side. Copy the input from the input arrays directly to the debug array and make sure that the data is arriving as you expect
• Follow the execution through writing the intermediate results to the debug array.
• Keep the execution constrained using barriers so that you know where the cores are up to when you write to the debug array

A few tips that I picked up along the way:

• If you get a Segmentation Fault there is a problem on the host side. It could show up during a clmsync but it is a host problem. I found that it was usually that I had not allocated enough space in an array.
• If there is a problem on the accelerator side the kernel will not return. Kill the waiting host program, scratch your head for a while and try again.
• Initialise your arrays so that you can tell the difference between a calculated value that is wrong and a memory location that has not been written to. When you are dealing with lots of floats, the rubbish that is in the array before it is used looks very much like wrong answers.

Up Next...

I hope this summary of what I've learnt to date on a "serious" working kernel is of assistance to you. If you want to know more about the neural network side I'll annotate the code on github which hopefully give you a better idea of how it works.

From here I'm going to move onto the simplest implementation of the back propagation algorithm that I can write. Given that the feed forward algorithm is the first part of the training sequence followed by back propagation I'll have to figure out how best to get these two to work together. Along with that I'll figure out how the performance benchmarking works so that as I work on increasing the performance I'll be able to tell if my efforts have paid off.

As always, please let me know if I've missed anything of if you have any questions...