Jordan Fix will present his FPO "Hardware MultiThreaded Transactions: Enabling Speculative MuliThreaded Pipeline Parallelization For Complex Programs" on Tuesday, 11/12/2019 at 10am in CS 402. The members of his committee are: David August (adviser); Readers: David August and David Callahan (Reservoir Labs); Examiners: Margaret Martonosi, David Wentzlaff (ELE), and Andrew Appel. A copy of his thesis is available upon request. Please email ngotsis@cs.princeton if you would like a copy of the thesis. Everyone is invited to attend his talk. The talk abstract follows below: Speculation with transactional memory systems helps programmers and compilers produce profitable thread-level parallel programs. Prior work shows that supporting transactions that can span multiple threads, rather than requiring transactions be contained within a single thread, enables new types of speculative parallelization techniques for both programmers and parallelizing compilers. Unfortunately, software support for multi-threaded transactions (MTXs) comes with significant additional inter-thread communication overhead for speculation validation. This overhead can make otherwise good parallelization unprofitable for programs with sizeable read and write sets. Some programs using these prior software MTXs overcame this problem through significant efforts by expert programmers to minimize these sets and optimize communication, capabilities which compiler technology has been unable to achieve to date. Instead, this dissertation makes speculative parallelization less laborious and more feasible through low-overhead speculation validation, presenting the first complete design, implementation, and evaluation of hardware MTXs. Even with maximal speculation validation of every load and store inside transactions of tens to hundreds of millions of instructions, profitable parallelization of complex programs can be achieved. Across 8 benchmarks, this system achieves a geomean speedup of 104% over sequential execution on a multicore machine with 4 cores, with modest increases in power and energy consumption. This work could be used as a building block to enable more realistic automatic parallelization of complex programs, by providing low-overhead, long-running, resilient transactions that support a diverse set of parallel paradigm options.