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Signed-off-by: Nick Sarnie <[email protected]>
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sycl/doc/design/ThinLTO.md

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@@ -91,7 +91,7 @@ However, the flow is not ideal for many reasons:
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backend. We could slightly improve this issue by using a library call to the
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SPIR-V translator instead of the tool, however the library API requires setting
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up an object to represent the arguments while we only have strings, and it's
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non-trivial to parse the trings to figure out how to create the argument
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non-trivial to parse the strings to figure out how to create the argument
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object. Since we plan to use the SPIR-V backend in the long term, this does not
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seem to be worth the effort.
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@@ -104,9 +104,9 @@ when we actually are going to run codegen.
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fully linked module to accurately compute metadata, see the above explanation
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of SYCL requirement 2). We could get around storing the module by computing the
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metadata inside the LTO framework and storing it for late use by the SYCL
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bundling code, but doing this would require SYCL-only customizations including
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bundling code, but doing this would require even more SYCL-only customizations including
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even more new function arguments and modifications of the `OffloadFile` class.
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It's also complicated because the LTO framework is multithreaded, and not all
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There are also compliations because the LTO framework is multithreaded, and not all
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LLVM data structures are thread safe.
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The proposed long-term SYCL ThinLTO flow is as follows:
@@ -117,7 +117,7 @@ The biggest difference here is that we are running codegen using the SPIR-V
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backend.
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Also, instead of using a lambda function in the `PreCodeGenModuleHook`
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callback, we can take advantage of the `PreCodeGenPassesHook` field to add
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callback to run SYCL finalization passes, we can take advantage of the `PreCodeGenPassesHook` field to add
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passes to the pass manager that the LTO framework will run.
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It is possible that the number of device images in the fat executable

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