Device performance in OpenCL DES

Among various computing devices I have there is one that stands out it is NVIDIA Quadro NVS 140M because it supports only FP32 (float) operations, but not FP64 (double). It is generally too old. In OpenCL we have both pow function which takes double and float parameters. The latter is called pown. I use first one to actually benchmark double precision computation.

ModelYearCoreUnitClkPerf1k10k100k
NVS 4200M20114811480156/12131161163
Tesla K20xm20122688147323935/13122324
Intel i7 2640M2011242800n/a327281
RTX 3050 Ti Mobile202125602016955299/8321090
Intel UHD 10200H2020192121050422/?419192
NVS 140M (FP32)200716280025/-474454453

The fastest in this comparison is Tesla K20xm which I find a little surprising because it is from 2012 and it wins over RTX 3050 Ti Mobile from 2021. However if we take into consideration that FP64 performance of Tesla is 15 times greater (only 4 x in actual time) than RTX then it should be obvious why it wins.

I have no need to use double to be honest (integer should be just fine here), but it is a great chance to see performance differences between various devices. Using FP32 would be quite difficult to get such a broad range of timings. Using pown(float, integer) changes above table a little bit as we start using FP32 computations (at 100k elements):

  • Tesla K20xm: 12ms
  • RTX 3050 Ti Mobile: 3ms
  • NVS 4200m: 352ms
  • NVS140M: 4453ms

Now I look at those timings from theoretical performance measured in GFLOPS. Comparing NVS 4200M and NVS 140M we have relation of approx. 6 times (156 vs 25), but timing relation is only just close to 4. So other factors come to play here also. Comparing RTX 3050 Ti and Tesla K20xm we have 1.34 (5299 vs 3935), but timing relation is 4. So actual performance gain is much higher than I would expect comparing GFLOPS measurements.

Getting Tesla K20xm is a steal in terms of FP64 computations as it is on similar level as RTX 4090.

Text processing in OpenCL

OpenCL is excellent in the field of numbers, but not that much into text processing. It lacks even basic functions available in regular C99. So the question is if it is worth trying to process some text in it.

In my OpenCL base project (which can be found here) I’ve added “aiml” module. It loads over 31k lines of text with over 4 mln characters. The text itself is in the first buffer of uchar array. Second buffer holds pointers and lenghts of consecutive lines being work-items, so there are over 31k of such work-items. Third buffer is a result array when I can store a outcome of kernel processing.

Java base code

First read text file and generate pointers and lenghts:

    public void readFile() {
        System.out.println(" - Read file");
        File file = new File("aiml/src/com/michalasobczak/aiml/bible.txt");
        try {
            BufferedReader br = new BufferedReader(new FileReader(file));
            String st = null;
            while ((st = br.readLine()) != null) {
                allstring.append(st);
                vector.add(st);
                line_sizes.add(pointer);
                pointer = pointer + st.length();
                counter++;
            }
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
        System.out.println("Read no of lines: " + counter);
        System.out.println("sample line: " + vector.get(100));
        System.out.println("sample line size in chars: " + line_sizes.get(5));
        n = counter;
    }

And then copy source text, pointers and lenghts to thebuffers:

    public void generateSampleRandomData() {
        System.out.println(" - Started sampling data");
        for (int i=0; i<allstring.length(); i++) {
            srcArrayA[i] = (byte)allstring.charAt(i);
        }
        System.out.println("allstring size: " + allstring.length());
        for (int i=0; i<n; i++) {
            srcArrayB[i*2]  = line_sizes.get(i);
            srcArrayB[(i*2)+1] = vector.get(i).length();
        }
        System.out.println(" - Finished sampling data");
    }

Buffers are as follows:

    public void createBuffers() {
        // Allocate the memory objects for the input- and output data
        this.memObjects[0] = clCreateBuffer(this.context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, (long) Sizeof.cl_uchar * this.allstring.length(), KernelConfigurationSet.srcA, null);
        this.memObjects[1] = clCreateBuffer(this.context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, (long) Sizeof.cl_int * this.n * 2, KernelConfigurationSet.srcB, null);
        this.memObjects[2] = clCreateBuffer(this.context, CL_MEM_READ_WRITE, (long) Sizeof.cl_uchar * this.n, null, null);
    }

Kernel code is divided into two parts:

    public void readKernelFile() {
        this.content = new String("");
        try {
            this.content = Files.readString(Path.of("aiml/src/com/michalasobczak/aiml/utils.c"));
            this.content += "\n";
            this.content += Files.readString(Path.of("aiml/src/com/michalasobczak/aiml/kernel.c"));

        } catch (IOException e) {
            e.printStackTrace();
        }
    }

In terms of Java base code that is all.

C99 kernel code

As I have already mentioned, OpenCL C99 lacks of several basic text processing features. I think that is because it is not meant to be used with texts and secondly because you cannot do recursion and I suspect that some of those functions might use it. So I decided to prepare some basic functions as follows:

#define MAX_STRING_SIZE 256

int get_string_size(uchar string[]) {
    int size;
    int c;
    int counter = 0;
    for (c=0; c<MAX_STRING_SIZE; c++) {
        if (string[c] == NULL) {
            break;
        }
        counter++;
    }
    return counter;
}


void print_string(uchar stringa[]) {
    int size = get_string_size(stringa);
    int c;
    for (c=0; c<size; c++) {
        printf("%c", stringa[c]);
    }
    printf("\n");
}


void set_string(uchar wb[], uchar tmp[]) {
    int c;
    int size = get_string_size(tmp);
    for (c=0; c<size; c++) {
        wb[c] = tmp[c];
    }
}

void set_string_and_print(uchar wb[], uchar tmp[]) {
    set_string(wb, tmp);
    print_string(wb);
}

int find_string_in_string(uchar source[], uchar looking_for[]) {
    int s_size = get_string_size(source);
    int lf_size = get_string_size(looking_for);
    int c, d;
    for (c=0; c<s_size; c++) {
        for (d=0; d<lf_size; d++) {
            if (source[c+d] == looking_for[d]) {
                ;
            }
            else {
                break;
            }
            if (d == lf_size-1) {
                return 1;
            }
        } 
    } 
    return 0;
}

Few words of explanation. String size function relies on NULL terminated characters array. String setter function does not puts that NULL in the end, so you need to do it yourself if needed. Finding string in string returns only first hit.

Now, the kernel:

__kernel void sampleKernel(__global const uchar* srcA,
                           __global const int2*   srcB,
                           __global       uchar*  dst)
{
    int gc, ls, gs, gid, lid;
    int c, d;
    int res = 0;
    __private uchar current[131*1000]    = { '\0' };
    __private uchar word_buffer[30] = { '\0' };
    // -- PROCEDURE
    gid = get_global_id(0);
    int2 val = srcB[gid];
    d = 0;
    // prepare current local item
    for (c=val.s0; c<val.s0+val.s1; c++) {
        current[d] = srcA[c];
        d++;
    } // for
    uchar tmp[10] = "LORD\0";
    set_string(word_buffer, tmp);
    res = find_string_in_string(current, word_buffer);
    dst[gid] = res;
} // kernel

As shown before in Java base code, there are 3 buffers. First one is for plain uchar array of source text. Second one is for int2 vectors holding pointers and lenghts of consecutive, adjacent lines/verses. Third is for output data, for instance in case of successful search it holds 1, otherwise 0.

I’ve tested this on my NVIDIA GeForce RTX 3050 Ti Mobile with 4 GB of VRAM. Having around 32k elements (work-items) means that we can allocate as much as 131kB of data per single work-item. This way we can fully load all available VRAM memory. Of source not all of given work-items will be run at the same time because there is only 2560 cores in this GPU. So obiously it is the maximum parallel items working at the “same time”. Estimated 13 rounds is required to process all the data, however we need to keep in mind that local-size is set to 32 and there are some specific constraints put on the GPU itself by CC (compute capabilities) specifications.

For CC 8.6 we have maximum of 16 thread blocks per SM (streaming multiprocessor) times 32 work-items of local-work-size it gives us 512 max. RTX 3050 Ti has 20 SM, so the maximum simultaneous (in theory) working items would be 10240, but having only 2560 cores I think that of course it will not reach that far having 100% utilization at much lower values. Still for the latest GPUs, they can have up to 16k cores, so that kind of hardware could better utilize CC 8.6 of higher specification on full load.

I would like to point out one more things regarding private/register memory and a global memory. In case of Ampere GPU architecture:

  • The register file size is 64K 32-bit registers per SM.
  • The maximum number of registers per thread is 255.

So, we are limited per work-item to 255 registers and there is also a 64k limit per SM. We can thus estimate or even calculate the maximum data size which will fit locally and beyond that value it will go outside to global memory providing much higher latency. It can be seen on times calculation increasing while we increase uchar current array.

Conclusion

Text processing in OpenCL works just fine with 0 – 1 ms per single search thru over 4 mln characters (31-32k lines). We are constrained by lack of string or memory functions so all string function I’ve made use constant array buffers. I’ve practically tested full VRAM allocation (4GB). Power draw is 51W.

What next? I think that may take step forward and try to do come classification and few other things toward ML or even AI. That can be quite interesting to see…

DES cipher is 40 times faster in OpenCL

OpenCL implementation od DES cipher is way faster than regular single-threaded C program. 1 mln encryptions take less than a second on RTX 3050 Ti, but also as much as almost 40 seconds on Intel i5-10200h single-thread application.

Lack of compact and extremely portable SSL/TLS library in pre C++11 project made me think about going for something easy to implement on my own concerning data encryption. I’ve selected DES, which stands for Data Encryption Standard because of my general understanding of such algorithm. It comes from 1975 and has certain limitations including short key length or known weak keys. But if we put our prejudices aside we may see few interesting things and opportunities as well.

It took me several days to accomplish C99 kernel in OpenCL. Before this I tried few third party examples in c++. One major drawback is that all of them use strings, vectors, stringstreams and few other strictly c++ features. Even use of printf is problematic in OpenCL implementations as you may not get it or it may be working differently from implementation to implementation. You will not be able to use some of c99 features like malloc/free. So to get maximum compatibility I went down to the simplest solutions.

I especially admire example in which you use binary as strings (uchar arrays). This way you can easily see what is going on. Of course (really?) it adds complexity and increases instructions count as well as memory consumption but for the first DES implementation it is acceptable. So you will see in various places arrays of 8 byte elements meaning 64 bits of data. Keys and other values as 56 or 48 bits of data and finally halves as 32 bits values (4 byte digits). Both input and output can be displayed as characters. Input will be plain ASCII, but output come over 128 decimal ASCII code so you can see some weird characters in case of printing them instead of presenting only numbers.

OpenCL vs single threaded C

In order to run kernel code outside OpenCL runtime you need to provide few things:

#include "stdio.h"
#include <math.h>

typedef unsigned char uchar;

You need to add math library in GCC invocation because by default it is not included:

gcc des.c -lm

Then, kernel main function need to be adjusted, for instance as:

void kernel() { 
  // here we have the kernel function body...
}

And finally provide C language main function with iterations already parametrized:

int main() {
    int i;
	for (i=0; i<1024*1024; i++) {
		if (i % 1024 == 0) {
			printf("i: %i ", i);
		}
		kernel();
	}
}

For sake of simplicity I skip uchar8 definition and handling as it do not add than much to overall complexity of the code and the algorithm. Running on different hardware with 1024*1024 iterations. First going to compare CPU execution time:

HardwareDuration
Dell G15: Intel Core i5 10200H 2.4 GHz38s
MacBookPro3,1: Intel Core 2 Duo 2.2 GHz1min 37s
PowerBook G4: PowerPC G4 800 MHz11 min 22s

Now compare it with OpenCL runtime duration:

HardwareCores NoCompute Units (acc. to OpenCL)Duration
NVIDIA GeForce RTX 3050 Ti Mobile256020930ms
Intel UHD 10th gen192122065ms

Java base project

I use OpenCL base project which can be found here. There is one additional module called “des”. Originally I used Java 17, but today I select Java 19. Frankly speaking I cannot point out easily anything that much important between Java 11 and 19. Each version introduces either small language changes or no changes at all. But if you code complex object-oriented applications then those changes might be interesting for you.

So… first I fill source data table with the following function:

    public void generateSampleRandomData() {
        Random rd = new Random();
        for (int i = 0; i <= (n*8) - 1; i++) {
            byte c = (byte)('a' + rd.nextInt(10));
            srcArrayA[i] = c;
        }
    }

This function generates n*8 byte elements within a range of ASCII ‘a’ letter decimal representation and 10 numbers ahead. In other words random characters will be within range from ‘a’ do ‘j’ which in decimal will be from 97 to 106. One word about byte type in Java language – it is always signed so there is direct possibility to use it as unsigned. There is however Byte.toUnsignedInt function which translates negative byte numbers into positives.

Next thing is buffer. As later we will see that kernel function utilizes uchar8 data type, there is need to map such type in Java. I came with idea of using plain byte array (byte[]). Each and every kernel invocation will map consecutive groups of 8 elements from this plain array:

   public void createBuffers() {
        // Allocate the memory objects for the input- and output data
        this.memObjects[0] = clCreateBuffer(this.context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, (long) Sizeof.cl_uchar8 * this.n, KernelConfigurationSet.srcA, null);
        this.memObjects[1] = clCreateBuffer(this.context, CL_MEM_READ_WRITE, (long) Sizeof.cl_uchar8 * this.n, null, null);
    }

Both source and target arrays are type cl_uchar8 which translate into uchar8 in the kernel itself. To print results coming back from kernel invocations we will use aforemendtioned Byte.toUnsignedInt function:

public void printResults() {
        System.out.println("Elements: " + dstArray.length);
        int i = 0;
        for (byte tmp : dstArray) {
            System.out.println(String.valueOf(i) + " : " + (Byte.toUnsignedInt(tmp)) );
            i++;
        }
}

And that is basically all regarding Java part of this challange. Use of Java here is a matter of covenience as you may do it also using c or c++. I do not know by now about any discrepancies in JOCL library and some other libraries available.

OpenCL 1.1

In order to run kernel code on some older devices you need to adjust few things. First you need to get rid of printf function invocations or define it by yourself. Second things you need to enable floating point cl_khr_fp64 extension in case of using double type:

#pragma OPENCL EXTENSION cl_khr_fp64 : enable

There are options no to use pow function and convert the entire cipher algorithm to use bit selection. For my educational purposes however it is much easier to see what’s going on like that.

C99 kernel

I’ve divided the DES cipher algorithm into 27 functional points from FP00 to FP26. Some of them contains data only and other ones consists procedures.

General section

  • FP00 – auxilliary functions (lines 1-133)
  • FP01 – kernel definition
  • FP02 – data and identification
  • FP03 – key
  • FP04 – plain text
  • FP05 – PC1/PC2 tables data

Generating keys

  • FP06 – PC1 key permutation
  • FP07 – key division into two equal halves
  • FP08 – 16 round keys, for 1, 2, 9 and 16 single left shifts
  • FP09 – for other iteration double left shifts
  • FP10 – combining left and right parts
  • FP11 – PC2 key permutation

Encryption procedure

  • FP12 – data only, initial permutation and expansion table data
  • FP13 – data only, substitution boxes 8 boxes, 4 rows, 16 colums each
  • FP14 – data only, permutation table
  • FP15 – applying initial permutation
  • FP16 – dividing result into two equal halves
  • FP17 – encrypting 16 times, right laft is expanded
  • FP18 – result XORed with key
  • FP19 – apply 8 times substitution boxes
  • FP20 – applying permutation
  • FP21 – result XORed with left half
  • FP22 – left and right part of plain text swapped
  • FP23 – combining left and right part
  • FP24 – applying inverse permutation
  • FP25 – preparing final result
  • FP26 – writing result to the output OpenCL buffer

Summary

In this challange… which source code can be found here I verified possibility to code DES cipher algorithm using OpenCL enabled devices. This 500 lines of code can be run either on OpenCL device or in slightly modified form on any other devices with can compile C language. OpenCL implementation runs 40 times faster on GPU than on single threaded CPU. This was kinda interesing one…

UnsupportedClassVersionError

Changing Java language level to run compiled code on older runtimes

I’m working on some Java project. I use IntelliJ IDEA and deciced to go with Oracle’s OpenJDK-18. But… this runtime is available by default only during compilation in the IDE. In the system I have OpenJDK-11. Trying to run code compiled by JDK-18 on JDK-11 gives me the following message:

java.lang.UnsupportedClassVersionError: Main has been compiled by a more recent version of the Java Runtime (class file version 62.0), this version of the Java Runtime only recognizes class file versions up to 55.

To overcome this go to module settings (or Project Structure) and change Language Level to lower value. In case you compile with 18 then it will have version 18 set. Switch to version 11 (local variables syntax for lambda parameters) and compile once again. Now you will be able to launch your application even with older JDK.