A Hierarchical Profiler of Intermediate Representation Code based on LLVM

Profiling based techniques have gained much attention on computer architecture and software analysis communities. The target is to rely on one or more profiling tools in order to identify specific code pieces of interest e.g., code pieces that slowdown a given application. The extracted code pieces can be further modified and optimized. In general, the profiling tools can be classified as deterministic, statistical-based, or rely on hardware performance counters. A common characteristic of the available profiling tools is typically based on analyzing or even manipulating (in case of binary instrumentation tools) machine-level code. This approach come with two main drawbacks. First, a lot of information (even GBytes of data) needs to be gathered, stored, post-processed, and visualized. Second, the performed analysis of the gathered data is platform-specific and it is not straightforward to categorize the given applications/program phases/kernels into distinct categories that have the same or almost the same behavior (e.g., the same percentage of computational vs. control instructions). The latter stems from the fact even small changes in the source code of the applications might lead to significantly different machine code implementations. Therefore, even two specific program kernels exhibit the same behavior (e.g., they have the same number of instructions, but with a different ordering), it is very difficult for a machine-code level profiling tool to assess their similarity, simply because the generated machine level code might have significant differences resulting in many missing opportunities for the available profiling tools. To address this issue, in this paper, we present a new profiling tool that is able to operate on the machine independent intermediate representation (IR) level. The profiler (still in development phase) relies on the LLVM API and it is able to hierarchically (at various levels of the call stack) and recursively parse the IR code and extract various useful statistics. We showcase the practicality of our profiler by analyzing a subset of the PolyBench benchmarks assuming (as pointed out by a recent study) that there is a strong correlation of LLVM IR code

Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome

BACKGROUND: Coronary artery inflammation inhibits adipogenesis in adjacent perivascular fat. A novel imaging biomarker—the perivascular fat attenuation index (FAI)—captures coronary inflammation by mapping spatial changes of perivascular fat attenuation on coronary computed tomography angiography (CTA). However, the ability of the perivascular FAI to predict clinical outcomes is unknown. METHODS: In the Cardiovascular RISk Prediction using Computed Tomography (CRISP-CT) study, we did a post-hoc analysis of outcome data gathered prospectively from two independent cohorts of consecutive patients undergoing coronary CTA in Erlangen, Germany (derivation cohort) and Cleveland, OH, USA (validation cohort). Perivascular fat attenuation mapping was done around the three major coronary arteries—the proximal right coronary artery, the left anterior descending artery, and the left circumflex artery. We assessed the prognostic value of perivascular fat attenuation mapping for all-cause and cardiac mortality in Cox regression models, adjusted for age, sex, cardiovascular risk factors, tube voltage, modified Duke coronary artery disease index, and number of coronary CTA-derived high-risk plaque features. FINDINGS: Between 2005 and 2009, 1872 participants in the derivation cohort underwent coronary CTA (median age 62 years [range 17–89]). Between 2008 and 2016, 2040 patients in the validation cohort had coronary CTA (median age 53 years [range 19–87]). Median follow-up was 72 months (range 51–109) in the derivation cohort and 54 months (range 4–105) in the validation cohort. In both cohorts, high perivascular FAI values around the proximal right coronary artery and left anterior descending artery (but not around the left circumflex artery) were predictive of all-cause and cardiac mortality and correlated strongly with each other. Therefore, the perivascular FAI measured around the right coronary artery was used as a representative biomarker of global coronary inflammation (for prediction of cardiac mortality, hazard ratio [HR] 2·15, 95% CI 1·33–3·48; p=0·0017 in the derivation cohort, and 2·06, 1·50–2·83; p<0·0001 in the validation cohort). The optimum cutoff for the perivascular FAI, above which there is a steep increase in cardiac mortality, was ascertained as −70·1 Hounsfield units (HU) or higher in the derivation cohort (HR 9·04, 95% CI 3·35–24·40; p<0·0001 for cardiac mortality; 2·55, 1·65–3·92; p<0·0001 for all-cause mortality). This cutoff was confirmed in the validation cohort (HR 5·62, 95% CI 2·90–10·88; p<0·0001 for cardiac mortality; 3·69, 2·26–6·02; p<0·0001 for all-cause mortality). Perivascular FAI improved risk discrimination in both cohorts, leading to significant reclassification for all-cause and cardiac mortality. Interpretation The perivascular FAI enhances cardiac risk prediction and restratification over and above current state-of-the-art assessment in coronary CTA by providing a quantitative measure of coronary inflammation. High perivascular FAI values (cutoff ≥–70·1 HU) are an indicator of increased cardiac mortality and, therefore, could guide early targeted primary prevention and intensive secondary prevention in patients. FUNDING: British Heart Foundation, and the National Institute of Health Research Oxford Biomedical Research Centre