fatbin | Compress executable and its resources
kandi X-RAY | fatbin Summary
kandi X-RAY | fatbin Summary
Instead of shipping a ZIP containing resources (images, sounds, etc.) and an executable, fatbin permits to compress everything in an unique executable file. It's my entry to the GopherGala 2016.
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Top functions reviewed by kandi - BETA
- readFiles reads the contents of the bufio . Reader into dstDir .
- BuildFatbin builds a fatbin binary for the given executable .
- parseDirectory parses the given directory .
- Parse a fatbin file
- RunFatbin runs the fatbin
- extractData extracts data part from a file .
- parseFlags parses the command line flags .
- main is the main function
- writeFile writes a file to dst
- Extracts data from a file
fatbin Key Features
fatbin Examples and Code Snippets
Community Discussions
Trending Discussions on fatbin
QUESTION
Before device link-time optimization (DLTO) was introduced in CUDA 11.2, it was relatively easy to ensure forward compatibility without worrying too much about differences in performance. You would typically just create a fatbinary containing PTX for the lowest possible arch and SASS for the specific architectures you would normally target. For any future GPU architectures, the JIT compiler would then assemble the PTX into SASS optimized for that specific GPU arch.
Now, however, with DLTO, it is less clear to me how to ensure forward compatibility and maintain performance on those future architectures.
Let’s say I compile/link an application using nvcc with the following options:
Compile
...ANSWER
Answered 2021-May-17 at 09:07According to an NVIDIA employee on the CUDA forums the answer is "not yet":
Good question. We are working on support for JIT LTO, but in 11.2 it is not supported. So in the example you give at JIT time it will JIT each individual PTX to cubin and then do a cubin link. This is the same as we have always done for JIT linking. But we should have more support for JIT LTO in future releases.
QUESTION
I am trying to optimize a CUDA code with LLVM passes on a PowerPC system (RHEL 7.6 with no root access) equipped with V100 GPUs, CUDA 10.1, and LLVM 11 (built from source). Also, I tested clang, lli, and opt on a simple C++ code, and everything works just fine.
After days of searching, reading, and trials-and-errors, I managed to compile a simple CUDA source. The code is the famous axpy:
...ANSWER
Answered 2021-Apr-17 at 16:29The problem was not related to PowerPC architecture. I needed to pass the fatbin file to the host-side compilation command with -Xclang -fcuda-include-gpubinary -Xclang axpy.fatbin to replicate the whole compilation behavior.
Here is the corrected Makefile:
QUESTION
Situation: I am trying to use cuModuleLoad to load the current binary's (ELF) embedded cubin (and PTX), but it keep erroring out with error code 200. My question is, if the cubin is embedded into the final binary, why can't I use cuModuleLoad to dynamically load ones self? It works when the I compile a separate fatbinary, but not when I load a separate PTX module, and of course when I try to load the final binary (a.out). I have a few reasons why I want to load the current executable that I will forgo to not go off topic. I am also looking for a workaround that maintains a single file without using utility tools (or system calls).
In Linux:
...ANSWER
Answered 2020-Nov-14 at 04:42Found a solution. In a nutshell :
- fopen( argv[0] )
- mmap ( file )
- Read the ELF headers and find the ".nv_fatbin" section
- Parse the ".nv_fatbin" aligning to byte sequence "50 ed 55 ba 01 00 10 00"
- Find the cubin related to the global method you want to cuModuleGetFunction
- Call cuModuleLoadFatBinary with a base address of the .nv_fatbin + specific cubin offset.
- Get the function using cuModuleGetFunction
- Finally call cuLaunchKernel
See sloppy code below for reference:
QUESTION
As part of a larger CMake project, I am adding a CUDA library. The rest of the project is C++, compiled with clang.
To test that the library works correctly, I'm creating a small executable and linking the CUDA library to it:
...ANSWER
Answered 2020-Jul-08 at 15:02I couldn't reproduce this issue in a fresh, tiny CMake project, so I eventually figured out that some flag from my larger project wasn't playing along.
It turns out that Thin LTO, which was enabled in CMAKE_CXX_FLAGS is causing this issue.
I disabled it for this particular target with:
QUESTION
I keep getting an "invalid device function" on my kernel launch. Google turns up a plethora of instances for this, however all of them seem to be related to a mismatch of the embedded SASS/PTX code embedded in the binary.
The way I understand how it works is:
- SASS code can only be interpreted by an GPU with the exact same SM version 2
- PTX code is forward-compatible, i.e. any newer GPU will be able to run the code (however, driver needs to JIT) 2
- I need to specify what I want to target by passing suitable -arch commands to
nvcc:-gencode arch=compute_30,code=sm_30will create a SASS targeting SM 3.0,-gencode arch=compute_60,code=compute_60will create PTX code 1 - To use cuda with static and shared libraries, I need to compile for position-independent code and enable separable compilation
What I did now is:
ANSWER
Answered 2019-Sep-16 at 08:19Ultimately, as expected, this was due to a build system setup problem.
TLDR version:
I managed to fix it by changing the library with my CUDA code from STATIC to SHARED.
To fix it, I first used the automatic architecture detection from FindCuda CMake (which seems to have create SM 6.1, so I was at lest right there)
QUESTION
I already read about virtual architecture and code generation for nvcc but I still have some questions.
I have a cuda compiled executable whose cuobjdump output is
ANSWER
Answered 2019-Sep-09 at 10:13
- What does code version mean? Documentation doesn't say that.
It means the version of the fatbin element it is printing -- elf version 1.7 and PTX version 5.0 respectively (see here for PTX versions)
- Would such an executable be compatible on a system with a sm_30 (Kepler) device?
Yes. The presence of the PTX (version 5.0) means the code can be JIT compiled by the driver to assembler to run on a compute capability 3.0 device (again documentation here)
QUESTION
I am working with some c++/CUDA code that makes significant use of templates for both classes and functions. We have mostly been using CUDA 9.0 and 9.1, where everything compiles and runs fine. However, compilation fails on newer versions of CUDA (specifically 9.2 and 10).
After further investigation, it seems that trying to compile exactly the same code with CUDA version 9.2.88 and above will fail, whereas with CUDA version 8 through 9.1.85 the code compiles and runs correctly.
A minimal example of the problematic code can be written as follows:
...ANSWER
Answered 2019-Feb-02 at 00:26This is a bug in CUDA 9.2 and 10.0 and a fix is being worked on. Thanks for pointing it out.
One possible workaround as you've already pointed out would be to revert to CUDA 9.1
Another possible workaround is to repeat the offending template instantiation in the body of the function (e.g. in a discarded statement). This has no impact on performance, it just forces the compiler to emit code for that function:
QUESTION
I want my compiled CUDA code to work on any Nvidia GPU, so I compile each .cu file with the options:
...ANSWER
Answered 2018-Jun-29 at 07:44The tool chain doesn't support this and you shouldn't expect to be able to do this by hand as nvcc does either.
However, you can certainly script some sort process to
- Execute parallel compilation of the code to multiple cubin files, one for each target architecture
- Perform a device link pass to combine the cubins to a single elf payload
- Link the final executable with the resulting object file emitted by the device link phase
You will probably need to enable separate device code compilation and you might also need to refactor your code slightly as a result. Caveat Emptor and all that.
QUESTION
Cannot reinstall most recent Torch. Cloning fresh repo and attempting to install via install.sh which performs a series of make calls results in:
ANSWER
Answered 2017-May-30 at 19:18It depends on what tmp is.
Sometimes, as an optimization, tmp is mounted in a ramdisk. You can take a look at that using mount or in /etc/fstab.
If this is not the case, then make sure the disk partition where /tmp is has enough space, or delete other unused temporary files.
BleachBit, packaged in many distros, can help you freeing space.
QUESTION
__constant__ const unsigned int *ff = (const unsigned int[]){90, 50, 100};
int main()
{
}
ANSWER
Answered 2017-Dec-26 at 08:49The compiler is telling you exactly what the error is. When you do this:
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