Virtualization challenges: a view from server consolidation perspective
VEE '12 Proceedings of the 8th ACM SIGPLAN/SIGOPS conference on Virtual Execution Environments
The HELIX project: overview and directions
Proceedings of the 49th Annual Design Automation Conference
Dynamic trace-based analysis of vectorization potential of applications
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Logical inference techniques for loop parallelization
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
HELIX: automatic parallelization of irregular programs for chip multiprocessing
Proceedings of the Tenth International Symposium on Code Generation and Optimization
Code generation for parallel execution of a class of irregular loops on distributed memory systems
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
JIT technology with C/C++: Feedback-directed dynamic recompilation for statically compiled languages
ACM Transactions on Architecture and Code Optimization (TACO)
Hi-index | 0.00 |
It is well known that a large fraction of applications cannot be parallelized at compile time due to unpredictable data dependences such as indirect memory accesses and/or memory accesses guarded by data-dependent conditional statements. A significant body of prior work attempts to parallelize such applications using runtime data-dependence analysis and scheduling. Performance is highly dependent on the ratio of the dependence analysis overheads with respect to the actual amount of parallelism available in the code. We have found that the overheads are often high and the available parallelism is often low when evaluating applications on a modern multicore processor. We propose a novel software-based approach called dependence-aware scheduling to parallelize loops with unknown data dependences. Unlike prior work, our main goal is to reduce the negative impact of dependence computation, such that when there is not an opportunity of getting speedup, the code can still run without much slowdown. If there is an opportunity, dependence-aware scheduling is able to yield very impressive speedup. Our results indicate that dependence-aware scheduling can greatly improve performance, with up to 4x speedups, for a number of computation intensive applications. Furthermore, the results also show negligible slowdowns in a stress test, where parallelism is continuously detected but not exploited.