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The Liberty Research Group

Parallelization Techniques with Improved Dependence Handling [abstract] (PDF)
Easwaran Raman
Ph.D. Thesis, Department of Computer Science, Princeton University, June 2009.

Continuing exponential growth in transistor density and diminishing returns from the increasing transistor count have forced processor manufacturers to pack multiple processor cores onto a single chip. These processors, known as multi-core processors, generally do not improve the performance of single-threaded applications. Automatic parallelization has a key role to play in improving the performance of legacy and newly written single-threaded applications in this new multi-threaded era.

Automatic parallelizations transform single-threaded code into a semantically equivalent multi-threaded code by preserving the dependences of the original code. This dissertation proposes two new automatic parallelization techniques that differ from related existing techniques in their handling of dependences. This difference in dependence handling enables the proposed techniques to outperform related techniques.

The first technique is known as parallel-stage decoupled software pipelining (PS-DSWP). PS-DSWP extends pipelined parallelization techniques like DSWP by allowing certain pipelined stages to be executed by multiple threads. Such a parallel execution of pipelined stages requires distinguishing inter-iteration dependences of the loop being parallelized from the rest of the dependences. The applicability and effectiveness of PS-DSWP is further enhanced by applying speculation to remove some dependences.

The second technique, known as speculative iteration chunk execution (Spice), uses value speculation to ignore inter-iteration dependences, enabling speculative execution of chunks of iterations in parallel. Unlike other value-speculation based parallelization techniques, Spice speculates only a few dynamic instances of those inter-iteration dependences.

Both these techniques are implemented in the VELOCITY compiler and are evaluated using a multi-core Itanium 2 simulator. PS-DSWP results in a geometric mean loop speedup of 2.13 over single-threaded performance with five threads on a set of loops from five benchmarks. The use of speculation improves the performance of PS-DSWP resulting in a geometric mean loop speedup of 3.67 over single-threaded performance on a set of loops from six benchmarks. Spice shows a geometric mean loop speedup of 2.01 on a set of loops from four benchmarks. Based on the above experimental results and qualitative and quantitative comparisons with related techniques, this dissertation demonstrates the effectiveness of the proposed techniques.