clMath is an open-source project provided by AMD. It contains a clBLAS library (source code). I checked the repo, and found out that all functions are written in C, not in OpenCL. Did I looking at wrong files? Where are the OpenCL kernels? How can a C function be used in parallel computing?
Actually, there is a folder named clTemplates and all .cl files are in this folder. I suppose that when a function is called, it will generate a .cl file based on one of the files in clTemplates folder, right? Hence, there are only 39 basic OpenCL kernels.
More generally, I want to know how does AMD SDK work?
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I wonder what kind of compiler compiles .cl files when we call clBuildProgram() API during the runtime? Is that depends on the device?
When you create a program from source and call clBuildProgram(), OpenCL runtime performs on-line compilation of the source. Each OpenCL runtime from the vendor includes OpenCL C compiler. Usually, the compiler is implemented as a shared library and supports only certain type of devices. For example, Intel OpenCL runtime for GPU uses Intel Graphics Compiler library to compile the source for Intel GPU devices.
I have my own linker and machine code converter.I am using my own assembly instruction for my machine.This machine is a software processor which executes machine code generated by asm to hex converter. Instead of assembly, i wan to use c language now.My question is that how to use LLVM for this purpose.
One approach could be that:
Create one parser which will read .s file (sort of asm file) generated by LLVM IR and map those instruction with my processor specific asm instruction.
I donot want to create linker and asm to machine code converter again.
Is my approach ok? or what could be the better way to do that.
The *.s file you read is not just "sort of asm", it is actually assembler that has already passed some LLVM backend, probably some X86 variant if you have not chosen a different target.
What you really want to do is to make LLVM emit assembly instructions for your own machine instead. This is what Writing an LLVM Backend and similar guides are about.
This is not exactly simple, but I expect that trying to translate some other machine's instruction set (let alone X86) to your own is probably even more difficult, as you would have to emulate each and every detail of a very complex machine.
I am sorry if this is a noob question but I am new to C++ and part of the reason I am messing with openCL is to learn more C++.
I installed the CUDA SDK and it put openCL header files here:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\include\CL
I added the the following two directories to additional include directories in Visual C++:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\include
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\include\CL
But when I try to reference anything in the cl namespace, like they do in this tutorial it does not work because cl is undefined.
This problem has already been solved so I'm only writing here to add some information.
Instead of using the Nvidia CUDA SDK you can use the Intel or AMD SDK (I prefer Intel). They both automatically include cl.hpp and support OpenCL 1.2 as well (Nvidia SDK only supports OpenCL 1.1). You may need to add #define CL_USE_DEPRECATED_OPENCL_1_1_APIS to make sure your kernel works on Nvidia devices.
The SDK has nothing to do with the device driver which compiles and runs the kernel. That is done by a vendor's video driver. In fact you can install the Nvidia video drivers, the AMD Radeon drivers (even if you don't have a AMD video card), and the Intel OpenCL drivers. Then you can compile your host code with e.g. the Intel OpenCL SDK and run your on kernel on Nvidia GPUs and Intel/AMD CPUs.
The problem is that nVidia's OpenCL framework (bundled with CUDA) doesn't come with the C++ wrapper library. But fortunately that one is a single header-only library using the existing OpenCL C API under the hood. So all you need to do is to download the official cl.hpp from Khronos and include it in your source file (after putting it into an accessible include directory, best together with nVidia's own OpenCL headers). In fact you don't need to include any other header once you include and use cl.hpp.
But be aware that this C++ wrapper only works for OpenCL 1.1 (and is anything but the best C++ wrapper one can come up with either), but nVidia doesn't have OpenCL 1.2 support anyway.
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How can I convert a JAR file to an EXE file?
Sorry if I sound like a newbie... But I am very new to the Java coding stuff.
Is there any way that you can convert an exe file to an executable JAR file? Like with Minecraft, there are a few versions of the launcher, some being JARs and some being EXEs. Please tell me if there is any way to run exes as JAR files, for example, the exe of GTA III or etc.
Files with a .exe extension simply imply that the file is an executable image, usually conforming to the Portable Executable standard. They consist of compiled code, native to the operating system and processor, in the form of assembly instructions that the processor can interpret. They may be originally written in almost any language - C, C#, C++, VB6, VB.NET, Delphi, x86 asm, Java, etc. For all intents and purposes (excluding .NET), you can't turn these into the original code.
JAR files are special archives containing compiled Java objects. These work in a similar way to executables, except they're handled by the Java Virtual Machine (JVM) rather than the operating system itself.
There's no way to turn compiled native code from an executable into a JAR. They're completely different concepts.
I'm not sure if it's possible. I want to study OpenCL in-depth, so I was wondering if there is a tool to disassemble an compiled OpenCL kernel.
For normal x86 executable, I can use objdump to get a disassembly view. Is there a similar tool for OpenCL kernel, yet?
If you're using NVIDIA's OpenCL implementation for their GPUs, you can do the followings to disassemble an OpenCL kernel:
Use clGetEventProfilingInfo() to dump the ptx code to a file, say ptxfile.ptx. Please refer to the OpenCL specification to have more details on this function.
Use nvcc to compile ptx to cubin file, for example: nvcc -cubin -arch=sm_20 ptxfile.ptx will compile ptxfile.ptx onto a compute capability 2.0 device.
Use cuobjdump to disassemble the cubin file into GPU instructions. For example: cuobjdump -sass ptxfile.cubin
Hope this helps.
I know that this is an old question, but in case someone comes looking here for disassembling a AMD GPU kernel, you can do the following in linux:
export GPU_DUMP_DEVICE_KERNEL=3
This make any kernel that is compiled on your machine dump the assembled code to a file in the same directory.
Source:
http://dis.unal.edu.co/~gjhernandezp/TOS/GPU/ATI_Stream_SDK_OpenCL_Programming_Guide.pdf
Sections 4.2.1 and 4.2.2
The simplest solution, in my experience, is to use clangs OpenCL C compiler and emit SPIR.
It even works on Godbolt's compiler explorer:
https://godbolt.org/z/_JbXPb
Clang can also emit ptx (https://godbolt.org/z/4ARMqM) and amdhsa (https://godbolt.org/z/TduTZQ), but it may not correspond to the ptx and amdhsa assembly generated by the respective driver at runtime.
If you work with an AMD GPU, you can use the Analyzer tool. It is free, cross-platform, and comes in two forms:
Command line tool (ships as part of the CodeXL package, search for the CodeXLAnalyzer executable after installing).
CodeXL GUI application (just switch to the Analyzer mode in CodeXL).
Here is a short summary of what you can do with the Analyzer:
Compile OpenCL kernels, OpenGL shaders and D3D shaders for any GPU that is supported by the installed driver (even without having the GPU physically installed on your system), and get the ISA. Using CodeXL Analyzer (option #2 above), you can get additional information such as an estimation for the number of clock cycles that are required to execute the instruction.
View the compiler-generated statistics (SGPRs usage, VGPRs usage, etc.)
Generate the AMD IL code for the OpenCL kernel.
Export the compiled binaries (ELF, in binary format).
You can download the CodeXL tool suite from here: https://gpuopen.com/compute-product/codexl/
As AMD CodeXLAnalyzer not not supported anymore use
Radeon GPU Analyzer