Apple Inc.

Eric Marshall Christopher

Christopher Arthur Lattner

Roger Scott Hoover

Victor Leonel Hernandez Porras

Thomas John O'Brien

Pratik Solanki

Jia-Hong Chen

Francois M Jouaux

Robert John McCall

Michael Alan Kahl

Observation and analysis based optimization of software code is disclosed. An expected value is chosen for a dynamic attribute that cannot be determined, prior to execution of the associated software code, to be guaranteed to have that expected value at runtime. An optimized version of the software code is generated, including one or more optimizations based on an assumption that the dynamic attribute will have the expected value. Non-exhaustive examples of a dynamic attribute include a variable type; a location in memory; a location in which a global object, property, or variable is stored; the contents of a global function or method; and a value of a global property or variable. A check is performed during execution of the optimized version of the software code, prior to executing the portion that has been optimized based on the assumption, to verify that the dynamic attribute has the expected value. In the event that it is determined at runtime that the dynamic attribute does not have the expected value, execution reverts to backup code that is not based on the assumption that dynamic attribute will have the expected value.

Observation and analysis based code optimization

Techniques for processing source code written in a traditionally interpreted language such as JavaScript, or another dynamic and/or interpreted language, are disclosed. In one example, compiled code associated with the source code is constructed and executed. An assumption on which a specific aspect of the compiled code is based (e.g., an optimization) is tested at a checkpoint of the compiled code. A roll over to fallback code is performed if the test indicates the assumption is not true.

Assumption-based compilation

Deferred constant pool generation is disclosed. Optimization processing is performed with respect to an intermediate representation of a source code. The optimized intermediate representation is used to generate a constant pool. In some embodiments, the source code comprises JavaScript, which is used to generate a low level virtual machine (LLVM) or other intermediate representation (IR), which intermediate representation is optimized prior to a constant pool being generated.

Deferred constant pool generation

A device-independent intermediate representation of a source code is generated and stored, e.g., in a memory or other storage mechanism. The stored intermediate representation of the source code is used to generate a device-specific machine code corresponding to the source code. The stored intermediate representation may be updated, e.g., periodically, for example by obtaining an updated version of the source code and compiling the updated source code to generate an updated intermediate representation. The stored intermediate representation may be based on source code received from a device that is synchronized with which a compiling device that generates the device-specific machine code. In some cases, the stored intermediate representation may be used to generate for each of a plurality of devices a corresponding device-specific machine code.

Converting JavaScript into a device-independent representation

Techniques for processing computer code are disclosed. In one example, an indication that a computer code is to begin execution at a portion of code other than a starting portion of the code is received, and a runtime state associated with the portion of the code at which execution is to begin is constructed. In some examples, execution of the portion of code is initiated. In some examples, a program counter associated with the portion of the code is used to initiate execution of the code. In some examples, the computer code comprises a fallback code associated with a previously executing code.

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Los Gatos, CA, United States of America

Eric Marshall Christopher