Three pitfalls in Java performance evaluation

The Java programming language has known a remarkable growth over the last decade. This is partially due to the infrastructure required to run Java ap- plications on general purpose microprocessors: a Java virtual machine (VM). The VM ensures that Java applications are portable across different hardware platforms, because it shelters the applications from the underlying system. Hence the motto write once, run (almost) anywhere. Java applications are compiled to an intermediate form, called bytecode, and consist of a number of so-called class files. The virtual machine takes care of class loading, interpreting or compiling the bytecode to the native code of the underlying hardware platform, thread scheduling, garbage collection, etc. As such, during the execution of a Java application, the VM regularly intervenes to take care of housekeeping tasks and to optimise the application as it is executing. Furthermore, the specific implementation details of most virtual machines insert non-deterministic behaviour, not into the semantic part of the execution, but rather into the lower level execution. For example, to bring a Java application up to competitive speed with classical compiled programs written in languages such as C, the virtual machine needs to optimise Java bytecode. To limit the execution overhead, most virtual machines use a time sampling mechanism to determine the hot methods in the application. This introduces non-determinism, as over several runs, the methods are not always optimised at the same moment, nor is the set of optimised methods always the same. Other factors that introduce non-determinism are the thread scheduling, garbage collection, etc. It is readily seen that performance analysis of Java applications is not as simple as it seems at first, and warrants closer inspection. In this dissertation we are mainly interested in the behaviour of Java applications and their performance. In the course of this work, we uncovered three major pitfalls that were not taken into account by researchers when analysing Java performance prior to this work. We will briefly summarise the main achievements presented in this dissertation. The first pitfall we present involves the interaction between the virtual machine, the application and the input to the application. The performance for short running applications is shown to be mainly determined by the virtual machine. For longer running applications, this influence decreases, but remains tangible. We use statistical analysis, such as principal components analysis and cluster analysis (K-means and hierarchical clustering) to demonstrate and clarify the pitfall. By means of a large number of performance char- acteristics measured using hardware performance counters, five virtual machines and fourteen benchmarks with both a small and a large input size, we demonstrate that short running workloads are primarily clustered by virtual machines. Even for long running applications from the SPECjvm98 benchmark suite, the virtual machine still exerts a large influence on the observed behaviour at the microarchitectural level. This work has shown the need for both larger and longer running benchmarks than were available prior to it – this was (partially) met by the introduction of the DaCapo benchmark suite – as well as a careful consideration when setting up an experiment to avoid measuring the virtual machine, rather than the benchmark. Prior to this work, people were quite often using simulation with short running applications (to save time) for exploring Java performance. The second pitfall we uncover involves the analysis of performance numbers. During a survey of 50 papers published at premier conferences, such as OOPSLA, PLDI, CGO, ISMM and VEE, over the past seven years, we found that a variety of approaches are used, both for experimental design – for example, the input size, virtual machines, heap sizes, etc. – and, even more importantly, for data analysis – for example, using a best out of 3 performance number. New techniques are pitted against existing work using these prevalent approaches, and conclusions regarding their successfulness in beating prior state-of-the-art are based upon them. Given the fact that the execution of Java applications usually involves non-determinism in the virtual machine – for example, when determining which methods to optimise – it should come as no surprise that the lack of statistical rigour in these prevalent approaches leads to misleading or even incorrect conclusions. By this we mean that the conclusions are either not representative of what actually happens, or even contradict reality, as modelled in a statistical manner. To circumvent this pitfall, we propose a rigorous statistical approach that uses confidence intervals to both report and compare performance numbers. We also claim that sufficient experiments should be conducted to get a reliable performance measure. The non-determinism caused by the timer-based optimisation component in a virtual machine can be eliminated using so-called replay compilation. This technique will record a compilation plan during a first execution or profiling run of the application. During a second execution, the application is iterated twice: once to compile and optimise all methods found in the compilation plan, and a second time to perform the actual measurement. It turns out however that current practice of using either a single plan – corresponding to the best performing profiling run – or a combined plan choosing the methods that were optimised in, say, more than half the profiling runs, is no match for using multiple plans. The variability observed in the plans themselves is too large to capture in one of the current practices. Consequently, using multiple plans is definitely the better option. Moreover, this allows using a matched-pair approach in the data analysis, which results in tighter confidence intervals for the mean performance number. The third pitfall we examine is the usage of global performance numbers when tuning either an application or a virtual machine. We show that Java applications exhibit phase behaviour at the method level. This means that instances of the same method show more similarity to each other, behaviourwise, than to instances of other methods. A phase can then be identified as a set of sub-trees of the dynamic call-tree, with each sub-tree headed by the same method. We present an two-step algorithm that allows correlating hardware performance counter data in step 2 with the phases determined in step 1. The information obtained can be applied to show the programmer which methods perform worse than average, for example with respect to the number of cache misses they incur. In the dissertation, we pay particular attention to statistical rigour. For each pitfall, we use statistics to demonstrate its presence. Hopefully this work will encourage other researchers to use more rigour in their work as well

A formalized framework for mobile cloud computing

Mobile Cloud Computing (MCC) is an emerging paradigm to transparently provide support for demanding tasks on resource-constrained mobile devices by relying on the integration with remote cloud services. Research in this field is tackling the multiple conceptual and technical challenges (e.g., how and when to offload) that are hindering the full realization of MCC. The Networked Autonomic Machine (NAM) framework is a tool that supports and facilitates the design networks of hardware and software autonomic entities, providing or consuming services or resources. Such a framework can be applied, in particular, to MCC scenarios. In this paper, we focus on NAM’s features related to the key aspects of MCC, in particular those concerning code mobility capabilities and autonomic offloading strategies. Our first contribution is the definition of a set of high-level actions supporting MCC. The second contribution is the proposal of a formal semantics for those actions, which provides the core NAM features with a precise formal characterization. Thus, the third contribution is the further development of the NAM conceptual framework and, in particular, the partial re-engineering of the related Java middleware. We show the effectiveness of the revised middleware by discussing the implementation of a Global Ambient Intelligence case study

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware Computing

Power consumption is rapidly becoming the dominant limiting factor for further improvements in computer design. Curiously, this applies both at the "high end" of workstations and servers and the "low end" of handheld devices and embedded computers. At the high-end, the challenge lies in dealing with exponentially growing power densities. At the low-end, there is a demand to make mobile devices more powerful and longer lasting, but battery technology is not improving at the same rate that power consumption is rising. Traditional power-management research is fragmented; techniques are being developed at specific levels, without fully exploring their synergy with other levels. Most software techniques target either operating systems or compilers but do not explore the interaction between the two layers. These techniques also have not fully explored the potential of virtual machines for power management. In contrast, we are developing a system that integrates information from multiple levels of software and hardware, connecting these levels through a communication channel. At the heart of this system are a virtual machine that compiles and dynamically profiles code, and an optimizer that reoptimizes all code, including that of applications and the virtual machine itself. We believe this introspective, holistic approach enables more informed power-management decisions

Performance analysis methods for understanding scaling bottlenecks in multi-threaded applications

In dit proefschrift stellen we drie nieuwe methodes voor om de prestatie van meerdradige programma's te analyseren. Onze eerste methode, criticality stacks, is bruikbaar voor het analyseren van onevenwicht tussen draden. Om deze stacks te construeren stellen we een nieuwe criticaliteitsmetriek voor, die de uitvoeringstijd van een applicatie opsplitst in een deel voor iedere draad. Hoe groter dit deel is voor een draad, hoe kritischer deze draad is voor de applicatie. De tweede methode, bottle graphs, stelt iedere draad van een meerdradig programma voor als een rechthoek in een grafiek. De hoogte van de rechthoek wordt berekend door middel van onze criticaliteitsmetriek, en de breedte stelt het parallellisme van een draad voor. Rechthoeken die bovenaan in de grafiek zitten, als het ware in de hals van de fles, hebben een beperkt parallellisme, waardoor we ze beschouwen als “bottlenecks” voor de applicatie. Onze derde methode, speedup stacks, toont de bereikte speedup van een applicatie en de verschillende componenten die speedup beperken in een gestapelde grafiek. De intuïtie achter dit concept is dat door het reduceren van de invloed van een bepaalde component, de speedup van een applicatie proportioneel toeneemt met de grootte van die component in de stapel

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

The Eureka Programming Model for Speculative Task Parallelism

In this paper, we describe the Eureka Programming Model (EuPM) that simplifies the expression of speculative parallel tasks, and is especially well suited for parallel search and optimization applications. The focus of this work is to provide a clean semantics for, and efficiently support, such "eureka-style" computations (EuSCs) in general structured task parallel programming models. In EuSCs, a eureka event is a point in a program that announces that a result has been found. A eureka triggered by a speculative task can cause a group of related speculative tasks to become redundant, and enable them to be terminated at well-defined program points. Our approach provides a bound on the additional work done in redundant speculative tasks after such a eureka event occurs. We identify various patterns that are supported by our eureka construct, which include search, optimization, convergence, and soft real-time deadlines. These different patterns of computations can also be safely combined or nested in the EuPM, along with regular task-parallel constructs, thereby enabling high degrees of composability and reusability. As demonstrated by our implementation, the EuPM can also be implemented efficiently. We use a cooperative runtime that uses delimited continuations to manage the termination of redundant tasks and their synchronization at join points. In contrast to current approaches, EuPM obviates the need for cumbersome manual refactoring by the programmer that may (for example) require the insertion of if checks and early return statements in every method in the call chain. Experimental results show that solutions using the EuPM simplify programmability, achieve performance comparable to hand-coded speculative task-based solutions and out-perform non-speculative task-based solutions

Proceedings of the 4th International Conference on Principles and Practices of Programming in Java

This book contains the proceedings of the 4th international conference on principles and practices of programming in Java. The conference focuses on the different aspects of the Java programming language and its applications