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https://github.com/JuliaGPU/OpenCL.jl
OpenCL Julia bindings
https://github.com/JuliaGPU/OpenCL.jl
julia opencl
Last synced: 8 days ago
JSON representation
OpenCL Julia bindings
- Host: GitHub
- URL: https://github.com/JuliaGPU/OpenCL.jl
- Owner: JuliaGPU
- License: other
- Created: 2013-10-26T21:34:26.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2024-02-27T19:58:57.000Z (4 months ago)
- Last Synced: 2024-06-18T21:45:58.521Z (18 days ago)
- Topics: julia, opencl
- Language: Julia
- Homepage:
- Size: 5.59 MB
- Stars: 263
- Watchers: 22
- Forks: 41
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Lists
- awesome-stars - JuliaGPU/OpenCL.jl - OpenCL Julia bindings (Julia)
README
### >> THIS PACKAGE NEEDS A MAINTAINER << ###
Please reach out to @juliohm if you would like to be the new maintainer.
# OpenCL.jl
[![][buildkite-img]][buildkite-url]
[buildkite-img]: https://badge.buildkite.com/6b2a46bff67692115dea3ad5a275d2f80777a5a99ffe42adb0.svg
[buildkite-url]: https://buildkite.com/julialang/opencl-dot-jl
*Julia interface for the OpenCL parallel computation API*
This package aims to be a complete solution for OpenCL programming in Julia, similar in scope to [PyOpenCL] for Python. It provides a high level API for OpenCL to make programing hardware accelerators, such as GPUs, FPGAs, and DSPs, as well as multicore CPUs much less onerous.
OpenCL.jl provides access to [OpenCL API](https://www.khronos.org/registry/OpenCL/) versions 1.0, 1.1, 1.2 and 2.0.
## Installation
1. Install an OpenCL driver. (If you're on macOS, OpenCL is either already available or unsupported.)
2. Add OpenCL to your Julia environment:
```julia
using Pkg
Pkg.add("OpenCL")
```
## Basic example: vector add
**Note:** We use `cl.create_compute_context()` here which only considers GPUs and CPUs.
```julia
using LinearAlgebra
using OpenCL
const sum_kernel = "
__kernel void sum(__global const float *a,
__global const float *b,
__global float *c)
{
int gid = get_global_id(0);
c[gid] = a[gid] + b[gid];
}
"
a = rand(Float32, 50_000)
b = rand(Float32, 50_000)
device, ctx, queue = cl.create_compute_context()
a_buff = cl.Buffer(Float32, ctx, (:r, :copy), hostbuf=a)
b_buff = cl.Buffer(Float32, ctx, (:r, :copy), hostbuf=b)
c_buff = cl.Buffer(Float32, ctx, :w, length(a))
p = cl.Program(ctx, source=sum_kernel) |> cl.build!
k = cl.Kernel(p, "sum")
queue(k, size(a), nothing, a_buff, b_buff, c_buff)
r = cl.read(queue, c_buff)
if isapprox(norm(r - (a+b)), zero(Float32))
@info "Success!"
else
@error "Norm should be 0.0f"
end
```
## More examples
You may want to check out the `examples` folder. Either `git clone` the repository to your local machine or navigate to the OpenCL.jl install directory via
```julia
using OpenCL
cd(joinpath(dirname(pathof(OpenCL)), ".."))
```
Otherwise, feel free to take a look at the Jupyter notebooks below
* [OpenCL Fractals]
* [GPU Buffer Transpose]
* [Low Level API]
[OpenCL Fractals]:http://nbviewer.ipython.org/7517923
[GPU Buffer Transpose]:http://nbviewer.ipython.org/7517952
[Low Level API]:http://nbviewer.ipython.org/7452048
## Credit
This package is heavily influenced by the work of others:
* [PyOpenCL] by Andreas Klockner
* [oclpb] by Sean Ross
* [Boost.Compute] by Kyle Lutz
* [rust-opencl]
[PyOpenCL]: http://mathema.tician.de/software/pyopencl/
[oclpb]: https://github.com/srossross/oclpb
[Boost.Compute]:https://github.com/kylelutz/compute
[rust-opencl]: https://github.com/luqmana/rust-opencl
## Documentation: API
Here's a rough translation between the OpenCL API in C to this Julia version. Optional arguments are indicated by `[name?]` (see `clCreateBuffer`, for example). For a quick reference to the C version, see [the Khronos quick reference card](https://www.khronos.org/files/opencl-1-2-quick-reference-card.pdf).
### Platform and Devices
| C | Julia | Notes |
|---------------------|-------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `clGetPlatformIDs` | `cl.platforms()` | |
| `clGetPlatformInfo` | `cl.info(platform, :symbol)` | Platform info: `:profile`, `:version`, `:name`, `:vendor`, `:extensions` |
| `clGetDeviceIDs` | `cl.devices()`, `cl.devices(platform)`, `cl.devices(:type)` | Device types: `:all`, `:cpu`, `:gpu`, `:accelerator`, `:custom`, `:default` |
| `clGetDeviceInfo` | `cl.info(device, :symbol)` | Device info: `:driver_version`, `:version`, `:profile`, `:extensions`, `:platform`, `:name`, `:device_type`, `:has_image_support`, `:queue_properties`, `:has_queue_out_of_order_exec`, `:has_queue_profiling`, `:has_native_kernel`, `:vendor_id`, `:max_compute_units`, `:max_work_item_size`, `:max_clock_frequency`, `:address_bits`, `:max_read_image_args`, `:max_write_image_args`, `:global_mem_size`, `:max_mem_alloc_size`, `:max_const_buffer_size`, `:local_mem_size`, `:has_local_mem`, `:host_unified_memory`, `:available`, `:compiler_available`, `:max_work_group_size`, `:max_work_item_dims`, `:max_parameter_size`, `:profiling_timer_resolution`, `:max_image2d_shape`, `:max_image3d_shape` |
| `clCreateContext` | `cl.context(queue)`, `cl.context(CLMemObject), `cl.context(CLArray)` | |
| `clReleaeContext` | `cl.release!` | |
### Buffers
| C | Julia | Notes |
|------------------------|-------------------------------------------------------------------------------------------------------------|------------------------------------------------------------|
| `clCreateBuffer` | `cl.Buffer(type, context, [length?]; [hostbuf?])`, `cl.Buffer(type, context, flags, [length?]; [hostbuf?])` | Memory flags: `:rw`, `:r`, `:w`, `:use`, `:alloc`, `:copy` |
| `clEnqueueCopyBuffer` | `cl.copy!(queue, destination, source)` | |
| `clEnqueueFillBuffer` | `cl.enqueue_fill_buffer(queue, buffer, pattern, offset, nbytesm wait_for)` | |
| `clEnqueueReadBuffer` | `cl.enqueue_read_buffer(queue, buffer, hostbuf, dev_offset, wait_for, is_blocking)` | |
| `clEnqueueWriteBuffer` | `cl.enqueue_write_buffer(queue, buffer, hostbuf, byte_count, offset, wait_for, is_blocking)` | |
### Program Objects
| C | Julia | Notes |
|-------------------------------|------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
| `clCreateProgramWithSource` | `cl.Program(ctx; source)` | |
| `clCreateProgramWithBinaries` | `cl.Program(ctx; binaries)` | |
| `clReleaseProgram` | `cl.release!` | |
| `clBuildProgram` | `cl.build!(progrm, options)` | |
| `clGetProgramInfo` | `cl.info(program, :symbol)` | Program info: `:reference_count`, `:devices`, `:context`, `:num_devices`, `:source`, `:binaries`, `:build_log`, `:build_status` |
### Kernel and Event Objects
| C | Julia | Notes |
| --- | --- | ----- |
| `clCreateKernel` | `cl.Kernel(program, "kernel_name")` | |
| `clGetKernelInfo` | `cl.info(kernel, :symbol)` | Kernel info: `:name`, `:num_args`, `:reference_count`, `:program`, `:attributes` |
| `clEnqueueNDRangeKernel` | `cl.enqueue_kernel(queue, kernel, global_work_size)`, `cl.enqueue_kernel(queue, kernel, global_work_size, local_work_size; global_work_offset, wait_on)` | |
| `clSetKernelArg` | `cl.set_arg!(kernel, idx, arg)` | `idx` starts at 1 |
| `clCreateUserEvent` | `cl.UserEvent(ctx; retain)` | |
| `clGetEventInfo` | `cl.info(event, :symbol)` | Event info: `:context`, `:command_queue`, `:reference_count`, `:command_type`, `:status`, `:profile_start`, `:profile_end`, `:profile_queued`, `:profile_submit`, `:profile_duration`
| `clWaitForEvents` | `cl.wait(event)`, `cl.wait(events)` |
| `clEnqueueMarkerWithWaitList` | `cl.enqueue_marker_with_wait_list(queue, wait_for)` | |
| `clEnqueueBarrierWithWaitList` | `cl.enqueue_barrier_with_wait_list(queue, wait_for)` | |