Search for: All records

Calling context is crucial for improving the precision of program analyses in various use cases (clients), such as profiling, debugging, optimization, and security checking. Often the calling context is encoded using a numerical value. We have observed that many clients benefit not only from a deterministic but also globally distinguishable value across runs to simplify bookkeeping and guarantee complete uniqueness. However, existing work only guarantees determinism, not global distinguishability. Clients need to develop auxiliary helpers, which incurs considerable overhead to distinguish encoded values among all calling contexts. In this paper, we propose Deterministic Distinguishable Calling Context Encoding () that can enable both properties of calling context encoding natively. The key idea of is leveraging the static call graph and encoding each calling context as the running call path count. Thereby, a mapping is established statically and can be readily used by the clients. Our experiments with two client tools show that has a comparable overhead compared to two state-of-the-art encoding schemes, PCCE and PCC, and further avoids the expensive overheads of collision detection, up to 2.1× and 50%, for Splash-3 and SPEC CPU 2017, respectively. 

Apache Spark arguably is the most prominent Big Data processing framework tackling the scalability challenge of a wide variety of modern workloads. A key to its success is caching critical data in memory, thereby eliminating wasteful computations of regenerating intermediate results. While critical to performance, caching is not automated. Instead, developers have to manually handle such a data management task via APIs, a process that is error-prone and labor-intensive, yet may still yield sub-optimal performance due to execution complexities. Existing optimizations rely on expensive profiling steps and/or application-specific cost models to enable a postmortem analysis and a manual modification to existing applications. This paper presents CACHEIT, built to take the guesswork off the users while running applications as-is. CACHEIT analyzes the program’s workflow, extracting important features such as dependencies and access patterns, using them as an oracle to detect high-value data candidates and guide the caching decisions at run time. CACHEIT liberates users from low-level memory management requirements, allowing them to focus on the business logic instead. CACHEIT is application-agnostic and requires no profiling or a cost model. A thorough evaluation with a broad range of Spark applications on real-world datasets shows that CACHEIT is effective in maintaining satisfactory performance, incurring only marginal slowdown compared to the manually well-tuned counterparts 

Thanks to recent advances in high-bandwidth, low-latency interconnects, running a data-intensive application with a remote memory pool is now a feasibility. When developing a data-intensive application, a managed language such as Java is often the developer’s choice due to convenience of the runtime such as automatic memory management. However, the memory management cost increases significantly in far memory due to remote memory accesses. Our insight is that data hotness (i.e., access frequency of objects) is the key to reducing the memory management cost and improving efficiency in far memory. In this paper, we present an ongoing work designing Polar, an enhanced runtime system that is hotness-aware, and optimized for far memory. In Polar, the garbage collector is augmented to identify cold (infrequently accessed) objects and relocate them to remote memory pools. By placing objects at memory locations based on their access frequency, Polar minimizes the number of remote accesses, ensures low access latency for the application, and thus improves overall performance. 

Abstract Cu is the most promising metal catalyst for CO₂electroreduction (CO₂RR) to multi-carbon products, yet the structure sensitivity of the reaction and the stability versus restructuring of the catalyst surface under reaction conditions remain controversial. Here, atomic scale simulations of surface energies and reaction pathway kinetics supported by experimental evidence unveil that CO₂RR does not take place on perfect planar Cu(111) and Cu(100) surfaces but rather on steps or kinks. These planar surfaces tend to restructure in reaction conditions to the active stepped surfaces, with the strong binding of CO on defective sites acting as a thermodynamic driving force. Notably, we identify that the square motifs adjacent to defects, not the defects themselves, as the active sites for CO₂RR via synergistic effect. We evaluate these mechanisms against experiments of CO₂RR on ultra-high vacuum-prepared ultraclean Cu surfaces, uncovering the crucial role of step-edge orientation in steering selectivity. Overall, our study refines the structural sensitivity of CO₂RR on Cu at the atomic level, highlights the self-activation mechanism and elucidates the origin of in situ restructuring of Cu surfaces during the reaction. 

The indirect exchange interaction between local magnetic moments via surface electrons has been long predicted to bolster the surface ferromagnetism in magnetic topological insulators (MTIs), which facilitates the quantum anomalous Hall effect. This unconventional effect is critical to determining the operating temperatures of future topotronic devices. However, the experimental confirmation of this mechanism remains elusive, especially in intrinsic MTIs. Here, we combine time-resolved photoemission spectroscopy with time-resolved magneto-optical Kerr effect measurements to elucidate the unique electromagnetism at the surface of an intrinsic MTI MnBi₂Te₄. Theoretical modeling based on 2D Ruderman-Kittel-Kasuya-Yosida interactions captures the initial quenching of a surface-rooted exchange gap within a factor of two but overestimates the bulk demagnetization by one order of magnitude. This mechanism directly explains the sizable gap in the quasi-2D electronic state and the nonzero residual magnetization in even-layer MnBi₂Te₄. Furthermore, it leads to efficient light-induced demagnetization comparable to state-of-the-art magnetophotonic crystals, promising an effective manipulation of magnetism and topological orders for future topotronics. 

Abstract Enzymes that oxidize aromatic substrates have shown utility in a range of cell-based technologies including live cell proximity labeling (PL) and electron microscopy (EM), but are associated with drawbacks such as the need for toxic H₂O₂. Here, we explore laccases as a novel enzyme class for PL and EM in mammalian cells. LaccID, generated via 11 rounds of directed evolution from an ancestral fungal laccase, catalyzes the one-electron oxidation of diverse aromatic substrates using O₂instead of toxic H₂O₂, and exhibits activity selective to the surface plasma membrane of both living and fixed cells. We show that LaccID can be used with mass spectrometry-based proteomics to map the changing surface composition of T cells that engage with tumor cells via antigen-specific T cell receptors. In addition, we use LaccID as a genetically-encodable tag for EM visualization of cell surface features in mammalian cell culture and in the fly brain. Our study paves the way for future cell-based applications of LaccID. 

Historical systematic exclusionary tactics based on race have forced people of certain demographic groups to congregate in specific urban areas. Aside from the ethical aspects of such segregation, these policies have implications for the allocation of urban resources including public transportation, healthcare, and education within the cities. The initial step towards addressing these issues involves conducting an audit to assess the status of equitable resource allocation. However, due to privacy and confidentiality concerns, individual-level data containing demographic information cannot be made publicly available. By leveraging publicly available aggregated demographic statistics data, we introduce PopSim, a system for generating semi-synthetic individual-level population data with demographic information. We use PopSim to generate multiple benchmark datasets for the city of Chicago and conduct extensive statistical evaluations to validate those. We further use our datasets for several case studies that showcase the application of our system for auditing equitable allocation of city resources. 

The deformation and breakup of water droplets impacted by a shock wave has been largely attributed to surface mechanisms. This study investigates the possibility of cavitation-induced droplet breakup. Shock waves of Mach 4 are used in this study to impact groups of droplets, both groups of degassed droplets and a group of non-degassed droplets. Distilled water droplets on the order of 1-3 mm in diameter are introduced into the shock tube. High speed images and deformation plots are used to explore the existence of cavitation in the droplets, as well as how they deform comparatively. 

Patients on continuous flow ventricular assist devices (CF-VADs) are at high risk for the development of Acquired von-Willebrand Syndrome (AVWS) and non-surgical bleeding. von Willebrand Factor (vWF) plays an essential role in maintaining hemostasis via platelet binding to the damaged endothelium to facilitate coagulation. In CF-VAD patients, degradation of vWF into low MW multimers that are inefficient in facilitating coagulation occurs and has been primarily attributed to the supraphysiological shear stress associated with the CF-VAD impeller. Methods In this review, we evaluate information from the literature regarding the unraveling behavior of surface-immobilized vWF under pulsatile and continuous flow pertaining to: (A) the process of arterial endothelial vWF production and release into circulation, (B) the critical shear stress required to unravel surface bound versus soluble vWF which leads to degradation, and (C) the role of pulsatility in on the production and degradation of vWF. Results and Conclusion Taken together, these data suggests that the loss of pulsatility and its impact on arterial endothelial cells plays an important role in the production, release, unraveling, and proteolytic degradation of vWF into low MW multimers, contributing to the development of AVWS. Restoration of pulsatility can potentially mitigate this issue by preventing AVWS and minimizing the risk of non-surgical bleeding.