Optimizing whole programs for code size

Reducing code size has benefits at every scale. It can help fit embedded software into strictly limited storage space, reduce mobile app download time, and improve the cache usage of supercomputer software. There are many optimizations available that reduce code size, but research has often neglected this goal in favor of speed, and some recently developed compiler techniques have not yet been applied for size reduction. My work shows that newly practical compiler techniques can be used to develop novel code size optimizations. These optimizations complement each other, and other existing methods, in minimizing code size. I introduce two new optimizations, Guided Linking and Semantic Outlining, and also present a comparison framework for code size reduction methods that explains how and when my new optimizations work well with other, existing optimizations. Guided Linking builds on recent work that optimizes multiple programs and shared libraries together. It links an arbitrary set of programs and libraries into a single module. The module can then be optimized with arbitrary existing link-time optimizations, without changes to the optimization code, allowing them to work across program and library boundaries; for example, a library function can be inlined into a plugin module. I also demonstrate that deduplicating functions in the merged module can significantly reduce code size in some cases. Guided Linking ensures that all necessary dynamic linker behavior, such as plugin loading, still works correctly; it relies on developer-provided constraints to indicate which behavior must be preserved. Guided Linking can achieve a 13% to 57% size reduction in some scenarios, and can speed up the Python interpreter by 9%. Semantic Outlining relies on the use of automated theorem provers to check semantic equivalence of pieces of code, which has only recently become feasible to perform at scale. It extends outlining, an established technique for deduplicating structurally equivalent pieces of code, to work on code pieces that are semantically equivalent even if their structure is completely different. My comparison framework covers a large number of different code size reduction methods from the literature, in addition to my new methods. It describes several different aspects by which each method can be compared; in particular, there are multiple types of redundancy in program code that can be exploited to reduce code size, and methods that exploit different types of redundancy are likely to work well in combination with each other. This explains why Guided Linking and Semantic Outlining can be effective when used together, along with some kinds of existing optimizations

Psychiatric Emergencies During the Height of the COVID-19 Pandemic in the Suburban New York City Area

BACKGROUND: This report characterizes patients presenting for psychiatric emergencies during the COVID-19 pandemic and describes COVID-19-related stressors. METHODS: Patients seen for emergency psychiatric evaluation during the height of the COVID-19 period (March 1-April 30, 2020; N = 201) were compared with those in the immediate Pre-COVID-19 period (January 1-February 28, 2020; N = 355), on sociodemographic characteristics, psychiatric diagnoses, symptoms, and disposition. Patients tested positive for COVID-19 were compared with those that tested negative on the same outcomes. Prevalence and nature of COVID-19-stressors that influenced the emergency presentation were rated. OUTCOME: The most common psychiatric diagnoses and presenting symptoms during both periods were depression and suicidal ideation. Comparing the Pre-COVID-19 and COVID-19 periods, a significant decline in emergency psychiatric volume was observed in children and adolescents (C/A), but not adults. COVID-19 period C/A patients had more new onset disorders and were more likely to be admitted to inpatient care, but were less likely to present with suicide attempts, impulse control disorders and agitation/aggression. Adults were more likely to have no access to outpatient care, present with anxiety disorders, and were also more likely to be admitted for inpatient care. COVID-19 directly affected the psychiatric emergency in 25% of patients, with the more severe stressors triggered by fear of COVID infection (including psychosis), actual COVID infection in self or family members, including death of a loved one. COVID-positive patients were more likely to have psychosis, including new-onset, and were less likely to be depressed/suicidal compared to their COVID-negative counterparts. CONCLUSION: This report demonstrates the need for emergency psychiatric services throughout the COVID-19 pandemic and the need for clinical and diagnostic COVID-19 screening of psychiatric emergency patients. New and severe pathology underscore the need for enhanced outpatient access to tele-mental health, crisis hotline and on-line psychotherapeutic services, as well as psychiatric inpatient services with capacity to safely care for COVID-19 patients

Characteristics of Calls to a COVID-19 Mental Health Hotline in the First Wave of the Pandemic in New York

This report describes the development, implementation and outcomes of a COVID-19 Anxiety Hotline, designed to address the community\u27s mental health crisis provoked by the coronavirus pandemic. The service was specifically designed using survey data regarding the effects of the COVID-19 pandemic on its staff and community members. Callers had around-the-clock direct access to mental healthcare providers at no cost. Quantitative analysis showed that nearly three out of four callers experienced new onset anxiety and insomnia driven by fear of exposure, and had difficulty accessing mental health care. In addition to immediate support, referral to tele-mental health care was provided to 86% of callers. Qualitative analysis indicates the effectiveness of immediate support and appropriate referrals using a tele-health platform. Our report indicates that the service was utilized by the general population, by health care workers, and rapidly provided referrals to individuals with limited access to mental health care during the pandemic