Exploiting speculative thread-level parallelism in data compression applications

Although hardware support for Thread-Level Speculation (TLS) can ease the compiler's tasks in creating parallel programs by allowing the compiler to create potentially dependent parallel threads, advanced compiler optimization techniques must be developed and judiciously applied to achieve the desired performance. In this paper, we take a close examination on two data compression benchmarks, GZIP and BZIP2, propose, implement and evaluate new compiler optimization techniques to eliminate performance bottlenecks in tíieir parallel execution and improve their performance. The proposed techniques (i) remove the critical forwarding path created by synchronizing memory-resident values; (ii) identify and categorize reduction-like variables whose intermediate results are used within loops, and propose code transformation to remove the inter-thread data dependences caused by these variables; and (iii) transform the program to eliminate stalls caused by variations in thread size. While no previous work has reported significant performance improvement on parallelizing these two benchmarks, we are able to achieve up to 36% performance improvement for GZIP and 37% for BZIP2.