Template:RidgeRun CUDA Optimisation Guide/Main contents: Difference between revisions

Template:RidgeRun CUDA Optimisation Guide/Main contents (view source)

Revision as of 17:43, 6 October 2021

1,518 bytes added , 6 October 2021

m

no edit summary

Spalli

Administrators

20,290

edits

@@ Line 17: / Line 17: @@
 | width="100%" valign="top" colspan="2"|
 <br>
+This manual does not try to replace either the [https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html Best Practices Guide] or the [https://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf Programming Guide]. Instead, it summarises some of their contents and encourages the developer to have a look at it in case of doubts. Additionally, it summarises some tips/hints presented in seminars and lectures in High-Performance Computing.
+The objective of this guide is to introduce the developers to accelerate algorithms that are currently working on GPU. This guide also examines some applications where GPU underperforms compared to other hardware accelerators such as the VIC (Vision Image Compositor), the NVDLA (NVIDIA Deep Learning Accelerator), ISP (Image Signal Processor) or, either, the CPU. It is highly recommended to be already familiar with the CUDA programming style.
+The manual will address:
+# '''The GPU Architecture''': a brief introduction to the GPU architecture, their strengths, and weaknesses.
+# '''A common workflow for optimisation''': it presents the common steps of the optimisation flow, in particular: code profiling, code analysis, debugging, and others.
+# '''Common optimisations''': evaluating when it is worth to continue. Additionally, common optimisations, including finer.
+# '''Pitfalls when optimising''': trying to optimise a non-optimisable application, some comments from people during code review, and so on.
+# '''Case studies''': stitching case in a brief.
 In this wiki, you will find technical documentation, tutorials, examples, and much more!