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Silicon is a critical element for diatom growth; however our understanding of the molecular mechanisms involved in intracellular silicon responses are limited. In this study, an iTRAQ-LC-MS/MS quantitative proteomic approach was coupled with an established synchrony technique to reveal the global metabolic silicon-response in the model diatom Thalassiosira pseudonana subject to silicon starvation and readdition. Four samples, which corresponded to the time of silicon starvation, girdle band synthesis, valve formation, and right after daughter cell separation (0, 1, 5, 7 h), were collected for the proteomic analysis. The results indicated that a total of 1,831 proteins, representing 16% of the predicted proteins encoded by the T. pseudonana genome, could be identified. Of the identified proteins, 165 were defined as being differentially expressed proteins, and these proteins could be linked to multiple biochemical pathways. In particular, a number of proteins related to silicon transport, cell wall synthesis, and cell-cycle progress could be identified. In addition, other proteins that are potentially involved in amino acid synthesis, protein metabolism, and energy generation may have roles in the cellular response to silicon. Our findings provide a range of valuable information that will be of use for further studies of this important physiological response that is unique to diatoms.

Conventional multicores rely on deep cache hierarchies to reduce data movement. Recent advances in die stacking have enabled near-data processing (NDP) systems that reduce data movement by placing cores close to memory. NDP cores enjoy cheaper memory accesses and are more area-constrained, so they use shallow cache hierarchies instead. Since neither shallow nor deep hierarchies work well for all applications, prior work has proposed systems that incorporate both. These asymmetric memory hierarchies can be highly beneficial, but they require scheduling computation to the right hierarchy. We present AMS, an adaptive scheduler that automatically finds high-quality thread-to-hierarchy mappings. AMS monitors threads, accurately models their performance under different hierarchies and core types, and adapts algorithms first proposed for cache partitioning to produce high-quality schedules. AMS is cheap enough to use online, so it adapts to program phases, and performs within 1% of an exhaustive-search scheduler. As a result, AMS outperforms asymmetry-oblivious schedulers by up to 37% and by 18% on average.

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