Name
Abstract | ||
|---|---|---|
Embedded processors have been increasingly exploiting hardware parallelism. Vector units, multiple processors or cores, hyper-threading, special-purpose accelerators such as DSPs or cryptographic engines, or a combination of the above have appeared in a number of processors. They serve to address the increasing performance requirements of modern embedded applications. How this hardware parallelism can be exploited by applications is directly related to the amount of parallelism inherent in a target application. In this paper we evaluate the performance potential of different types of parallelism, viz., true thread-level parallelism, speculative thread-level parallelism and vector parallelism, when executing loops. Applications from the industry-standard EEMBC 1.1, EEMBC 2.0 and the MiBench embedded benchmark suites are analyzed using the Intel C compiler. The results show what can be achieved today, provide upper bounds on the performance potential of different types of thread parallelism, and point out a number of issues that need to be addressed to improve performance. The latter include parallelization of libraries such as libc and design of parallel algorithms to allow maximal exploitation of parallelism. The results also point to the need for developing new benchmark suites more suitable to parallel compilation and execution. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1176254.1176298 | CODES+ISSS |
Keywords | Field | DocType |
thread parallelism,speculative thread-level parallelism,hardware parallelism,industry-standard eembc,performance potential,true thread-level parallelism,increasing performance requirement,loop parallelism,vector parallelism,different type,embedded application,mibench embedded benchmark suite,parallel algorithm,embedded systems,thread level speculation,upper bound,embedded processor,parallel processing,vectorization,programming model,programming models,multi threading,thread level parallelism,multithreading | Instruction-level parallelism,Multithreading,Implicit parallelism,Computer science,Task parallelism,Parallel computing,Speculative multithreading,Real-time computing,Data parallelism,Scalable parallelism,Memory-level parallelism | Conference |
ISBN | Citations | PageRank |
1-59593-370-0 | 9 | 0.51 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arun Kejariwal | 1 | 281 | 26.23 |
| Alexander V. Veidenbaum | 2 | 757 | 78.24 |
| Alexandru Nicolau | 3 | 2265 | 307.74 |
| Milind Girkarmark | 4 | 9 | 0.51 |
| Xinmin Tian | 5 | 596 | 52.92 |
| Hideki Saito | 6 | 177 | 14.88 |