Closed. This question needs to be more focused. It is not currently accepting answers.
Want to improve this question? Update the question so it focuses on one problem only by editing this post.
Closed 7 years ago.
Improve this question
Wikipedia Article on Mesa 3D says -
Mesa 3D - An open source implementation of OpenGL.
But as I see SGI has the entire OpenGL implementation source available online at their website, how is OpenGL not open-source or what is the need of Mesa 3D?
OpenGL is not Open Source. The NVIDIA and ATI implementations of OpenGL are not Open Source, whatever MESA or SGI's implementation doesn't change the fact that not all OpenGL implementations are Open Source.
OpenGL is a Open Specification, made by a comitee inside the Khronos industrial group (the ARB).
The SGI reference implementation is incredibly outdated, I think it only implements 1.2.
Mesa is being worked on and supports newer versions of the OpenGL specification. Currently it implements OpenGL 2.1 and a few extensions from OpenGL 3/4.
Mesa contains hardware accelerated drivers for many chips and is constantly updated. I think that SGI reference implementation is software-only.
As well as the other answers posted here — that Mesa is an ongoing project that is substantially more current than the SGI implementation — the initial release of Mesa predates the SGI release by something like seven years.
Related
Closed. This question is opinion-based. It is not currently accepting answers.
Want to improve this question? Update the question so it can be answered with facts and citations by editing this post.
Closed 3 years ago.
Improve this question
IDA pro ,x64Dbg, olldbg & windbg are used to Reverse Engineering purposes(as a Dissembler) and debugging.
What are the main differences among them? when to use each?
It's quite hard to give an answer that is not opinion-based. Trying to stay factual:
OllyDbg and x64Dbg falls roughly under the same category. Their main strength is debugging without symbolic information (although they can also do debugging with symbolic information). OllyDbg (closed source) hasn't been maintained since a long time now and is limited to x86 32-bit. On the other hand x64Dbg is actively maintained,open source and can handle x86 and x64. Both supports plugins.
Windbg is mainly a symbolic debugger (although obviously it works without any symbols). Being maintained by Microsoft it is very powerful when symbols are available. It supports plugins (in C or C++), scripting (it has its own scripting language but it also supports officially JavaScript and a third-party python scripts loader) .NET debugging and can do Kernel debugging (in this aspect it is the de facto Kernel debugger on Windows systems). In its latest version it also supports TTD (Time Travel Debugging).
IDA main strength is that it's an interactive disassembler. You can "interrogate" the binary (more precisely, the database generated from the binary) in many ways from python scripts. It also supports debugging by itself or through other engines (gdb or windbg engines for example).
Notable other professional dissassembler/debuggers are Binary Ninja (not free) and Ghidra (open source)
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
This question does not appear to be about a specific programming problem, a software algorithm, or software tools primarily used by programmers. If you believe the question would be on-topic on another Stack Exchange site, you can leave a comment to explain where the question may be able to be answered.
Closed 9 years ago.
Improve this question
Can I use nVidia CUDA on QNX(x86_64 or other) and what driver do I need for this?
I found nothing about this by link, and in #46 answer sound like "I don't know"
http://www.qnx.com/news/web_seminars/faq_multicore.html
But, QNX thinks to Include Support for NVIDIA Tegra Processor Family:
http://www.qnx.com/news/pr_5306_1.html
And nVidia thinks to add support CUDA and OpenCL to the nVidia Tegra5(Logan) ARM+GK1xx in next year:
http://en.wikipedia.org/wiki/Tegra#Logan
http://www.ubergizmo.com/2013/07/nvidia-tegra-5-release-date-specs-news/
And then can we use CUDA on nVidia Tegra5(ARM+GK1xx) on QNX(ARM), and what about QNX(x86)?
At this time, there's no support for CUDA on QNX.
The supported operating systems for CUDA are listed on the cuda download page as well as in section 1.4 of the release notes
Regarding Tegra, at this time there are no Tegra devices that support CUDA. The list of CUDA-enabled GPUs is here. Whether using an x86/x86_64 CPU or an ARM CPU, one of these CUDA GPUs is required for CUDA support.
Update: There are now tegra devices that support CUDA, including the widely available Tegra TK1 and recently announced TX1.
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
This question does not appear to be about programming within the scope defined in the help center.
Closed 6 years ago.
Improve this question
what is "SASS" short for ?
I know it is an asembly level native code ISA targeting specific hardware,
exits in between PTX code and binary code.
but anyone could kindly tell me what does each character stands for ?
all that i can find about Fermi hardware native instruction is in cuobjdump.pdf, but it only gives their names, where can I find more information such as their throughput or latency or IPC or CPI, undertaking units corresponding to each intruction, like SFU, FPU ?
Streaming ASSembler.... I should know since I invented the term, lead the core team G80 streaming process architecture team and developed first SASS assembler ;-)
Since there seems to be no information on this anywhere, I can only guess: Shader ASSembly language
SASS (as Ashwin points out probably "Shader ASSembly") is the binary code that runs on the metal of Fermi architecture devices. What cudaobjdump (and older third party tools like decuda and nv50dis) show is a direct disassembly of the cubin payload emitted by the ptxas assembler.
To the best of my knowledge there is no open instruction set documentation for any NVIDIA GPUs.
At some point during the CUDA 5 release cycle, NVIDIA began to provide a summary document which annotates the basic instruction set of suported GPUs (Fermi, Kepler, and Maxwell as of CUDA 7).
Streaming ASSembly? Since NVIDIA calls their cores, "Streaming Multiprocessors".
Closed. This question is off-topic. It is not currently accepting answers.
Want to improve this question? Update the question so it's on-topic for Stack Overflow.
Closed 10 years ago.
Improve this question
I want to download the latest version of the GPU computing SDK which is compatible with the system that I work on. The CUDA driver and runtime version are 4.10, but I can not find the link. I can just find the CUDA Toolkit which is not what I want. Can anyone help me by sending a direct link for me? Thanks.....
CUDA 4.1 is the latest CUDA release. The GPU Computing SDK for this release can be found at the bottom of this page: http://developer.nvidia.com/cuda-toolkit-41
The GPU Computing SDK is supposed to be available at this page: http://developer.nvidia.com/gpu-computing-sdk
But, it looks like NVIDIA has messed up the webpages a bit, the CUDA Toolkit and the GPU Computing SDK pages point at each other, with neither offering the SDK.
Closed. This question is off-topic. It is not currently accepting answers.
Want to improve this question? Update the question so it's on-topic for Stack Overflow.
Closed 10 years ago.
Improve this question
I know CUDA is for only Nvidia GPUs. If the specifications for a Nvidia graphics card states that CUDA is supported, can we conclude that it also supports OpenCL? Can there be support/compatibility problems with different OpenCL versions? i.e. OpenCL 1.0 or 1.1?
Given that in version 4.1 of the OpenCL Programming Guide (PDF) Nvidia explicitly speaks about CUDA-Enabled GPUs (Especially in Appendix A), I think we can safely assume that those cards supporting CUDA also support OpenCL. Even more so since OpenCL on Nvidia hardware still makes use of the CUDA architecture.
Appendix C of the same document lists the support of various extensions given a specific compute capability.
According to Nvidia's OpenCL page (http://developer.nvidia.com/opencl), all CUDA devices support OpenCL. As far as OpenCL versioning, that page indicates that you just need to make sure you have the right driver version.