Name
Abstract | ||
|---|---|---|
The stream-processing model is a natural fit for multicore systems because it exposes the inherent locality and concurrency of a program and highlights its separable tasks for efficient parallel implementations. We present flexible filters, a load-balancing optimization technique for stream programs. Flexible filters utilize the programmability of the cores in order to improve the data-processing throughput of individual bottleneck tasks by “borrowing” resources from neighbors in the stream. Our technique is distributed and scalable because all runtime load-balancing decisions are based on point-to-point handshake signals exchanged between neighboring cores. Load balancing with flexible filters increases the system-level processing throughput of stream applications, particularly those with large dynamic variations in the computational load of their tasks. We empirically evaluate flexible filters in a homogeneous multicore environment over a suite of five real-word stream programs. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2539036.2539041 | ACM Trans. Embedded Comput. Syst. |
Keywords | Field | DocType |
computational load,real-word stream program,load balancing,data-processing throughput,load-balancing optimization technique,homogeneous multicore environment,stream program,stream application,multicore system,flexible filter | Bottleneck,Locality,Handshake,Computer science,Concurrency,Load balancing (computing),Parallel computing,Real-time computing,Throughput,Multi-core processor,Scalability,Distributed computing | Journal |
Volume | Issue | ISSN |
13 | 3 | 1539-9087 |
Citations | PageRank | References |
0 | 0.34 | 37 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rebecca L. Collins | 1 | 172 | 10.47 |
| Luca P. Carloni | 2 | 1713 | 120.17 |