Name
Abstract | ||
|---|---|---|
Traditionally, programmers and software tools have focused on mapping a single data-parallel kernel onto a heterogeneous computing system consisting of multiple general-purpose processors (CPUS) and graphics processing units (GPUs). These methodologies break down as application complexity grows to contain multiple communicating data-parallel kernels. This paper introduces MKMD, an automatic system for mapping multiple kernels across multiple computing devices in a seamless manner. MKMD is a two phased approach that combines coarse grain scheduling of indivisible kernels followed by opportunistic fine-grained workgroup-level partitioning to exploit idle resources. During this process, MKMD considers kernel dependencies and the underlying systems along with the execution time model built with a few sets of profile data. With the scheduling decision, MKMD transparently manages the order of executions and data transfers for each device. On a real machine with one CPU and two different GPUs, MKMD achieves a mean speedup of 1.89x compared to the in-order execution on the fastest device for a set of applications with multiple kernels. 53% of this speedup comes from the coarse-grained scheduling and the other 47% is the result of the fine-grained partitioning. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/PACT.2015.14 | Parallel Architectures and Compilation Techniques |
Keywords | Field | DocType |
data-parallel kernel, scheduling, partitioning | Graphics,Kernel (linear algebra),Computer science,Scheduling (computing),Parallel computing,Symmetric multiprocessor system,Real-time computing,Exploit,Software,Schedule,Speedup | Conference |
ISSN | Citations | PageRank |
1089-795X | 6 | 0.46 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Janghaeng Lee | 1 | 316 | 11.34 |
| Mehrzad Samadi | 2 | 422 | 16.09 |
| Scott Mahlke | 3 | 4811 | 312.08 |