Cekirdekler

Very simple C# Multi-device GPGPU(OpenCL) compute API with an iterative interdevice-loadbalancing feature using multiple pipelining on read/write/compute operations for developers' custom opencl kernels.

Needs extra C++ dll built in 64-bit(x86_64) from https://github.com/tugrul512bit/CekirdeklerCPP which must be named KutuphaneCL.dll

The other needed dll is Microsoft's System.Threading.dll and its xml helper for .Net 2.0 - or - you can adjust "using" and use .Net 3.5+ for your own project and don't need System.Threading.dll.

In total, Cekirdekler.dll and KutuphaneCL.dll and using .Net 3.5 should be enough.

This project is being enhanced using ZenHub:

Features

• Implicit multi device control: from CPUs to any number of GPUs and ACCeelerators. Explicit in library-side for compatibility and performance, implicit for client-coder for the ease of GPGPU to concentrate on opencl kernel code.
• Iterative load balancing between devices: uniquely done for each different compute(explicit control with user-given compute-id). Multiple devices get more and more fair work loads until the ratio of work distribution converges to some point. Partitionig workload completes a kernel with less latency which is applicable for hot-spot loops and some simple embarrassingly-parallel algorithms. Even better for streaming data with pipelining option enabled.
• Pipelining for reads, computes and writes: either by the mercy of device drivers or explicit event-based queue management. Hides the latency of least time consuming part(such as writes) behind the most time consuming part(such as compute). GPUs can run buffer copies and opencl kernels concurrently.
• Working with different numeric arrays: Either C#-arrays like float[], int[], byte[],... or C++-array wrappers like ClFloatArray, ClArray<float>, ClByteArray, ClArray<byte>
• Automatic buffer copy optimizations for devices: If a device shares RAM with CPU, it uses map/unmap commands to reduce number of array copies(instead of read/write). If also that device is given a C++ wrapper array(such as ClArray<float>), it also uses cl_use_host_ptr flag on buffer for a zero-copy access aka" streaming". By default, all devices have their own buffers.
• Two different usage types: First one lets the developer choose all kernel parameters as arrays more explicitly for a more explicitly readable execution, second one creates same thing using a much shorter definition to complete in less code lines and change only the necessary flags instead of all.
• Automatic resource dispose: When C++ array wrappers are finalized(out-of-scope, garbage collected), they release resources. Also dispose method can be called explicitly by developer.
• Uses OpenCL 1.2: C++ bindings from Khronos.org for its base.

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Documentation

You can see details and tutorial here in Cekirdekler-wiki

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Known Issues

• For C++ array wrappers like Array<float> there is no out-of-bounds-check, don't cross boundaries when accessing array indexing.
• Don't use C++ array wrappers after they are disposed. These features are not added to speed-up array indexing.
• Don't use ClNumberCruncher or Core instances after they are disposed.
• Pay attention to "number of array elements used" per workitem in kernel and how they are given as parameters from API compute() method.
• Pay attenton to "partial read"/"read"/"write" array copy modifiers when your kernel is altering(or reading) whole array or just a part of it.
• No performance output at first iteration. Load balancer needs at least several iterations to distribute fairly and performance report needs at least 2 iterations for console output.

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Example

        Cekirdekler.ClNumberCruncher cr = new Cekirdekler.ClNumberCruncher(
            Cekirdekler.AcceleratorType.GPU, @"
                __kernel void hello(__global char * arr)
                {
                    printf(""hello world"");
                }
            ");

        Cekirdekler.ClArray<byte> array = new Cekirdekler.ClArray<byte>(1000);
        array.compute(cr, 1, "hello", 1000, 100);