Observable dynamic compilation

Managed language platforms such as the Java Virtual Machine rely on a dynamic compiler to achieve high performance. Despite the benefits that dynamic compilation provides, it also introduces some challenges to program profiling. Firstly, profilers based on bytecode instrumentation may yield wrong results in the presence of an optimizing dynamic compiler, either due to not being aware of optimizations, or because the inserted instrumentation code disrupts such optimizations. To avoid such perturbations, we present a technique to make profilers based on bytecode instrumentation aware of the optimizations performed by the dynamic compiler, and make the dynamic compiler aware of the inserted code. We implement our technique for separating inserted instrumentation code from base-program code in Oracle's Graal compiler, integrating our extension into the OpenJDK Graal project. We demonstrate its significance with concrete profilers. On the one hand, we improve accuracy of existing profiling techniques, for example, to quantify the impact of escape analysis on bytecode-level allocation profiling, to analyze object life-times, and to evaluate the impact of method inlining when profiling method invocations. On the other hand, we also illustrate how our technique enables new kinds of profilers, such as a profiler for non-inlined callsites, and a testing framework for locating performance bugs in dynamic compiler implementations. Secondly, the lack of profiling support at the intermediate representation (IR) level complicates the understanding of program behavior in the compiled code. This issue cannot be addressed by bytecode instrumentation because it cannot precisely capture the occurrence of IR-level operations. Binary instrumentation is not suited either, as it lacks a mapping from the collected low-level metrics to higher-level operations of the observed program. To fill this gap, we present an easy-to-use event-based framework for profiling operations at the IR level. We integrate the IR profiling framework in the Graal compiler, together with our instrumentation-separation technique. We illustrate our approach with a profiler that tracks the execution of memory barriers within compiled code. In addition, using a deoptimization profiler based on our IR profiling framework, we conduct an empirical study on deoptimization in the Graal compiler. We focus on situations which cause program execution to switch from machine code to the interpreter, and compare application performance using three different deoptimization strategies which influence the amount of extra compilation work done by Graal. Using an adaptive deoptimization strategy, we manage to improve the average start-up performance of benchmarks from the DaCapo, ScalaBench, and Octane suites by avoiding wasted compilation work. We also find that different deoptimization strategies have little impact on steady- state performance

An Empirical Study on Deoptimization in the Graal Compiler

Managed language platforms such as the Java Virtual Machine or the Common Language Runtime rely on a dynamic compiler to achieve high performance. Besides making optimization decisions based on the actual program execution and the underlying hardware platform, a dynamic compiler is also in an ideal position to perform speculative optimizations. However, these tend to increase the compilation costs, because unsuccessful speculations trigger deoptimization and recompilation of the affected parts of the program, wasting previous work. Even though speculative optimizations are widely used, the costs of these optimizations in terms of extra compilation work has not been previously studied. In this paper, we analyze the behavior of the Graal dynamic compiler integrated in Oracle\u27s HotSpot Virtual Machine. We focus on situations which cause program execution to switch from machine code to the interpreter, and compare application performance using three different deoptimization strategies which influence the amount of extra compilation work done by Graal. Using an adaptive deoptimization strategy, we managed to improve the average start-up performance of benchmarks from the DaCapo, ScalaBench, and Octane benchmark suites, mostly by avoiding wasted compilation work. On a single-core system, we observed an average speed-up of 6.4% for the DaCapo and ScalaBench workloads, and a speed-up of 5.1% for the Octane workloads; the improvement decreases with an increasing number of available CPU cores. We also find that the choice of a deoptimization strategy has negligible impact on steady-state performance. This indicates that the cost of speculation matters mainly during start-up, where it can disturb the delicate balance between executing the program and the compiler, but is quickly amortized in steady state

Why Does Theory of Inventive Problem Solving Matter in Malaysian Food and Beverage industry?

Nowadays, the competition in the food and beverage industry has developed from a single food competition to comprehensive competition in all aspects: from the pursuit of high-quality products to the expansion of market share, to the promotion of the brand effect and to the new shape of pursuing the entire enterprise. Consumers’ demands keep changing with time. These changes are different from demands that occur for classy foods requiring typical features such as nutrition value, the lusciousness of taste, and accessibility, to basic aspects such as improved food safety, food life span, durability, and waste food proper management. Thus, the objective of this research work is to review the TRIZ-theory of inventive problem-solving the matter in Malaysian food and beverage industry. The new product development in Malaysian food and beverage industry needs to accommodate the market demand. This study revealed that the actual product development process could be analyzed by assessing the interactions between consumer demands and expectations, the producer’s technical capacity of the food, and emerging knowledge from scientific researches about food. This has been attributed to the problem of the low quality of input terms of the food industries and productivity growth of Malaysian manufacturing industries, which is input-driven rather than total factor productivity-driven

Integration of Distinct Analysis Strategies Improves Tissue-Trait Association Identification

Integrating genome-wide association studies (GWAS) with transcriptomic data, human complex traits and diseases have been linked to relevant tissues and cell types using different methods. However, different results from these methods generated confusion while no gold standard is currently accepted, making it difficult to evaluate the discoveries. Here, applying three methods on the same data source, we estimated the sensitivity and specificity of these methods in the absence of a gold standard. We established a more specific tissue-trait association atlas by combining the information captured by different methods. Our triangulation strategy improves the performance of existing methods in establishing tissue-trait associations. The results provide better etiological and functional insights for the tissues underlying different human complex traits and diseases

Mediation modeling and analysis for high-throughput omics data

Abstract There is a strong need for powerful unified statistical methods for discovering underlying genetic architecture of complex traits with the assistance of omics information. In this paper, two methods aiming to detect novel association between the human genome and complex traits using intermediate omics data are developed based on statistical mediation modeling. We demonstrate theoretically that given proper mediators, the proposed statistical mediation models have better power than genome-wide association studies (GWAS) to detect associations missed in standard GWAS that ignore the mediators. For each of the modeling methods in this paper, an empirical example is given, where the association between a SNP and BMI missed by standard GWAS can be discovered by mediation analysis

Comprehensive Multiplatform Dynamic Program Analysis for Java and Android

Dynamic program analyses, such as profiling, tracing and bug-finding tools, are essential for software engineering. Unfortunately, implementing dynamic analyses for managed languages such as Java is unduly difficult and error-prone, because the run-time environments provide only complex low-level mechanisms. Currently, programmers writing custom tooling must expend great effort in tool development and maintenance, while still suffering substantial limitations such as incomplete code coverage or lack of portability. Ideally, a framework would be available in which dynamic analysis tools could be expressed at a high level, robustly, with high coverage and supporting alternative run-times such as Android. We describe our research on an \all-in-one" dynamic program analysis frame- work which uses a combination of techniques to satisfy these requirements

Genetic and phenotypic links between obesity and extracellular vesicles

Obesity has a highly complex genetic architecture, making it difficult to understand the genetic mechanisms, despite the large number of discovered loci via genome-wide association studies (GWAS). Omics techniques have provided a better resolution to view this problem. As a proxy of cell-level biology, extracellular vesicles (EVs) are useful for studying cellular regulation of complex phenotypes such as obesity. Here, in a well-established Scottish cohort, we utilized a novel technology to detect surface proteins across millions of single EVs in each individual’s plasma sample. Integrating the results with established obesity GWAS, we inferred 78 types of EVs carrying one or two of 12 surface proteins to be associated with adiposity-related traits such as waist circumference. We then verified that particular EVs’ abundance is negatively correlated with body adiposity, while no association with lean body mass. We also revealed that genetic variants associated with protein-specific EVs capture 2–4-fold heritability enrichment for blood cholesterol levels. Our findings provide evidence that EVs with specific surface proteins have phenotypic and genetic links to obesity and blood lipids, respectively, guiding future EV biomarker research

A comprehensive toolchain for workload characterization across JVM languages

The Java Virtual Machine (JVM) today hosts implementations of numerous languages. To achieve high performance, JVM implementations rely on heuristics in choosing compiler optimizations and adapting garbage collection behavior. Historically, these heuristics have been tuned to suit the dynamics of Java programs only. This leads to unnecessarily poor performance in case of non-Java languages, which often exhibit systematic differences in workload behavior. Dynamic metrics characterizing the workload help to identify and quantify useful optimizations, but so far, no cohesive suite of metrics has adequately covered properties that vary systematically between Java and non-Java workloads. We present a suite of such metrics, justifying our choice with reference to a range of guest languages. These metrics are implemented on a common portable infrastructure which ensures ease of deployment and customization