Deep learning can predict laboratory quakes from active source seismic data

Small changes in seismic wave properties foretell frictional failure in laboratory experiments and in some cases on seismic faults. Such precursors include systematic changes in wave velocity and amplitude throughout the seismic cycle. However, the relationships between wave features and shear stress are complex. Here, we use data from lab friction experiments that include continuous measurement of elastic waves traversing the fault and build data-driven models to learn these complex relations. We demonstrate that deep learning models accurately predict the timing and size of laboratory earthquakes based on wave features. Additionally, the transportability of models is explored by using data from different experiments. Our deep learning models transfer well to unseen datasets providing high-fidelity models with much less training. These prediction methods can be potentially applied in the field for earthquake early warning in conjunction with long-term time-lapse seismic monitoring of crustal faults, CO2 storage sites and unconventional energy reservoirs

On the Performance Potential of Different Types of Speculative Thread-Level Parallelism

Recent research in thread-level speculation (TLS) has proposed several mechanisms for optimistic execution of di#cultto -analyze serial codes in parallel. Though it has been shown that TLS helps to achieve higher levels of parallelism, evaluation of the unique performance potential of TLS, i.e., performance gain that be achieved only through speculation, has not received much attention. In this paper, we evaluate this aspect, by separating the speedup achievable via true TLP (thread-level parallelism) and TLS, for the SPEC CPU2000 benchmark. Further, we dissect the performance potential of each type of speculation --- control speculation, data dependence speculation and data value speculation. To the best of our knowledge, this is the first dissection study of its kind. Assuming an oracle TLS mechanism --- which corresponds to perfect speculation and zero threading overhead --- whereby the execution time of a candidate program region (for speculative execution) can be reduced to zero, our study shows that, at the loop-level, the upper bound on the arithmetic mean and geometric mean speedup achievable via TLS across SPEC CPU2000 is 39.16% (standard deviation = 31.23) and 18.18% respectively