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Heterogeneous architectures have become increasingly common. From co-packaging small and large cores, to GPUs alongside CPUs, to general-purpose heterogeneous-ISA architectures with cores implementing different ISAs. As diversity of execution cores grows, predictive models become of paramount importance for scheduling and resource allocation. In this paper, we investigate the capabilities of performance predictors in a heterogeneous-ISA setting, as well as the predictors’ effects on scheduler quality. We follow an unbiased feature selection methodology to identify the optimal set of features for this task, instead of pre-selecting features before training. Finally, we incorporate our findings in ML-based schedulers and evaluate their sensitivity to the underlying system’s level of heterogeneity. We show our schedulers to perform within 2-11% of an oracular scheduler across a variety of underlying heterogeneous-ISA multicore systems without modification. 

Last-Level Cache (LLC) attacks typically exploit timing side channels in hardware, and thus rely heavily on timers for their operation. Many proposed defenses against such side-channel attacks capitalize on this reliance. This paper presents PRIME+ABORT, a new cache attack which bypasses these defenses by not depending on timers for its function. Instead of a timing side channel, PRIME+ABORT leverages the Intel TSX hardware widely available in both server- and consumer-grade processors. This work shows that PRIME+ABORT is not only invulnerable to important classes of defenses, it also outperforms state-of-the-art LLC PRIME+PROBE attacks in both accuracy and efficiency, having a maximum detection speed (in events per second) 3× higher than LLC PRIME+PROBE on Intel’s Skylake architecture while producing fewer false positives.