ACCORDING TO RECENT estimates, computing and communications could account for 20% of energy usage globally by 2025. 1 This trend shows no sign of slowing. The annual growth in power consumption of Internet-connected devices is 20%. Data centers alone are now accounting for more than 3% of global emissions. Even if you are not worried about this trend on the mega scale, you are likely concerned with the power consumption of the devices in your pocket, on your wrist, and in your ears. Software, hardware, and network attributes all contribute to power usage, but little attention has been given to this topic by the information and communications technology (ICT) community. For example, as software engineers, we were never taught to consider, much less manage, the energy consumption of the software systems we created. Despite our lack of awareness and preparation, we are now facing an undeniable reality: the software community must learn to design for, monitor, and manage the energy usage of software. For this reason, we argue the need for energy-aware software
Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.
There are billions of lines of sequential code inside nowadays' software which do not benefit from the parallelism available in modern multicore architectures. Automatically parallelizing sequential code, to promote an efficient use of the available parallelism, has been a research goal for some time now.This work proposes a new approach for achieving such goal. We created a new parallelizing compiler that analyses the read and write instructions, and control-flow modifications in programs to identify a set of dependencies between the instructions in the program. Afterwards, the compiler, based on the generated dependencies graph, rewrites and organizes the program in a taskoriented structure. Parallel tasks are composed by instructions that cannot be executed in parallel. A work-stealing-based parallel runtime is responsible for scheduling and managing the granularity of the generated tasks. Furthermore, a compile-time granularity control mechanism also avoids creating unnecessary data-structures.This work focuses on the Java language, but the techniques are general enough to be applied to other programming languages.We have evaluated our approach on 8 benchmark programs against OoOJava, achieving higher speedups. In some cases, values were close to those of
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