Statistically Analyzing Execution Variance for Soft Real-Time Applications

Certain high-performance applications like multimedia and gaming have performance requirements beyond reducing program execution time. These applications have repetitive components whose desired performance characteristics are more naturally expressed using soft real-time theory with its probabilistic guarantees. However, for large complex gaming and multimedia applications, programmers typically avoid real-time constructs as they significantly constrain how the programmer can express functionality. Instead, such applications are developed as monolithic programs using conventional languages like C/C++. Here the soft real-time behavior of the application becomes an emergent quality rather than being enforced by design. Programmers must then tweak parameters/algorithms until the application's soft real-time behavior becomes acceptable. There are currently no analysis techniques that directly extract the soft real-time execution characteristics of monolithic applications written without the use of real-time constructs. We introduce a domain-agnostic profiling methodology that identifies program execution-contexts whose variant behavior most significantly affects the soft real-time characteristics of the application.