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Small tool, doc, sample enhancements.

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- Expand message when HIP version mismatch detected.
- Doc touchup.
- change sorting of hipBusBandwidth so byte results shown at top.
-

Change-Id: Ifb4e44a5fdfb65d59c4994b11e5f13385705f7e0
This commit is contained in:
Ben Sander
2016-09-22 17:51:52 -05:00
parent b7fcdc6121
commit 7bc1af2776
6 changed files with 21 additions and 11 deletions
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docs/markdown/hip_faq.md
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@@ -146,11 +146,11 @@ The tools also struggle with more complex CUDA applications, in particular those
- For Nvidia platforms, HIP requires Unified Memory and should run on a device which runs the CUDA SDK 6.0 or newer. We have tested the Nvidia Titan and K40.
### Does Hipify automatically convert all source code?
Typically, Hipify can automatically convert almost all run-time code, and the coordinate indexing device code.
Typically, Hipify can automatically convert almost all run-time code, and the coordinate indexing device code (i.e. threadIdx.x -> hipThreadIdx_x).
Most device code needs no additional conversion, since HIP and CUDA have similar names for math and built-in functions.
HIP currently requires manual addition of one more arguments to the kernel so that the host can communicate the execution configuration to the device.
The clang-hipify tool will automatically modify the kernel signature as needed (automating a step that used to be done manually)
Additional porting may be required to deal with architecture feature queries or with CUDA capabilities that HIP doesn't support.
Developers should always expect to perform some platform-specific tuning and optimization.
In general, developers should always expect to perform some platform-specific tuning and optimization.
### What is NVCC?
NVCC is Nvidia's compiler driver for compiling "CUDA C++" code into PTX or device code for Nvidia GPUs. It's a closed-source binary product that comes with CUDA SDKs.