Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-89740-8_9 | LCPC |
Keywords | Field | DocType |
statistically analyzing execution variance,soft real-time applications,real-time construct,multimedia application,monolithic application,variant behavior,large complex gaming,certain high-performance application,soft real-time behavior,soft real-time theory,soft real-time execution characteristic,soft real-time characteristic,real time | Programmer,Profiling (computer programming),Computer science,Parallel computing,Real-time computing,Execution time,Probabilistic logic | Conference |
Volume | ISSN | Citations |
5335 | 0302-9743 | 4 |
PageRank | References | Authors |
0.42 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tushar Kumar | 1 | 16 | 2.82 |
| Romain Cledat | 2 | 67 | 4.61 |
| Jaswanth Sreeram | 3 | 47 | 5.90 |
| Santosh Pande | 4 | 567 | 59.76 |