Microsoft Technology Licensing, LLC

Wenlei He

Ten H Tzen

Patrick W Sathyanathan

Ankit Asthana

Adrian Militaru

Pratap Joseph Chandar

A profile guided optimization compiler utilizes sample profile data including a control flow representation of a program having block counts associated with each basic block of the program, and edge counts associated with each control flow edge estimated from the block counts. The sample profile data utilizes correlation data to map the address of a sampled instruction from a fully optimized binary directly into a corresponding basic block of source code control flow of the program using a relative virtual address (RVA) that is associated with each source code basic block and the sampled instruction. The correlation data is able to differentiate multiple blocks on the same source code line and handle inlining and optimizations with greater precision and efficiency. The block counts are then used to guide the optimization of the program.

Sample driven profile guided optimization with precise correlation

Methods, systems, and computer program products are provided that enable incremental compilation of source code. Attributes of an intermediate language (IL) representation and a compiled representation of a source code are stored. Modified source code that is a revised version of the first source code is received. An IL representation of the modified source code is generated. Attributes of the revised intermediate IL and the stored attributes of the IL representation are compared to determine a first set of functions changed in the modified source code. A second set of functions in the first source code is determined that includes functions affected the determined first set of functions. The first and second sets of functions are compiled to generate a set of compiled functions. Compiled versions of the first and second sets are replaced in the compiled representation of the first source code with the set of compiled functions.

Incremental whole program compilation of code

Instead of performing local dataflow analyses on all procedures during a multi-file optimized code generation, those dataflow analyses are done only on a generally much smaller set of procedures that were actually impacted by source code edits. Incremental inter-procedural dataflow analysis (IIPDA) code identifies a set of procedures to be recompiled due to impact from one or more edits and does local dataflow analyses only on them. Results of the incremental approach for use in generating optimized code match the results of a more expensive exhaustive interprocedural dataflow analysis of all procedures, even when call graph structure has been changed by the edits. The impacted procedures are identified based on which procedures were edited, dataflow values, intermediate language representations, and a portion of the call graph.

Incremental interprocedural dataflow analysis during compilation

A transformation of software control flow to reduce the number of successor blocks in the control flow as well as the number of flow control elements. The control flow after transformation has more streamline code and less data-dependent control flow, which yields better runtime performance, and at the same time maintain functionally equivalent. Software control flow is improved by, for each data-dependent flow control element in the control flow graph, finding value narrowing points that would be sufficient that the exit control flow selected by that flow control element would be deterministic. The control flow is modified such that the control flow leads (without passing through the flow control element) from that found value narrowing point to the identified control flow that would be selected given the found value narrowing point. This method may be repeated proceeding from one flow control element to the next.

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Wenlei He