Search for: All records

An in‐depth study of donor–acceptor (D/A) interactions between the high‐dipole acceptor C₆₀F₁₈(A) and polycyclic aromatic hydrocarbon (PAH) donors—pyrene, perylene, and coronene—reveals a surprisingly strong PAH size influence on the D/A complex stoichiometry and ordering in co‐crystals. The crystallographic study shows the tendency of D/A mixtures to form stacked layered structures for the larger PAHs, perylene and coronene, while the role of aromatic π–π interactions diminishes, in contrast to the smaller pyrene/C₆₀F₁₈system. The behavior of the layered‐D/A assemblies is investigated by utilizing sequential deposition and co‐evaporation of C₆₀F₁₈and coronene on Au(111) surfaces. Scanning tunneling microscopy shows that the flat lying configuration adopted by coronene on the metal, which forms highly ordered close‐packed monolayers stabilized by the interaction between their π electrons and the high density of gold surface states, hinders the formation of the ordered assemblies of the corresponding co‐crystal. The influence of the substrate plus the critical role played by electronic and steric effects in the co‐crystal formation are believed to cause the lack of viability. However, it is remarkable that, on the surface, adsorbed single C₆₀F₁₈molecules are well centered on top of one coronene molecule, facilitating charge transfer between D and A molecules. 

Modern data center applications experience frequent branch mispredictions – degrading performance, increasing cost, and reducing energy efficiency in data centers. Even the state-of-the-art branch predictor, TAGE-SC-L, suffers from an average branch Mispredictions Per Kilo Instructions (branch-MPKI) of 3.0 (0.5-7.2) for these applications since their large code footprints exhaust TAGE-SC-L’s intended capacity. In this work, we propose Whisper, a novel profile-guided mechanism to avoid branch mispredictions. Whisper investigates the in-production profile of data center applications to identify precise program contexts that lead to branch mispredictions. Corresponding prediction hints are then inserted into code to strategically avoid those mispredictions during program execution. Whisper presents three novel profile-guided techniques: (1) hashed history correlation which efficiently encodes hard-topredict correlations in branch history using lightweight Boolean formulas, (2) randomized formula testing which selects a locally optimal Boolean formula from a randomly selected subset of possible formulas to predict a branch, and (3) the extension of Read-Once Monotone Boolean Formulas with Implication and Converse Non-Implication to improve the branch history coverage of these formulas with minimal overhead. We evaluate Whisper on 12 widely-used data center applications and demonstrate that Whisper enables traditional branch predictors to achieve a speedup close to that of an ideal branch predictor. Specifically, Whisper achieves an average speedup of 2.8% (0.4%-4.6%) by reducing 16.8% (1.7%-32.4%) of branch mispredictions over TAGE-SC-L and outperforms the state-ofthe-art profile-guided branch prediction mechanisms by 7.9% on average.