A Compiler and Runtime Infrastructure for Automatic Program Distribution

This paper presents the design and the implementation of a compiler and runtime infrastructure for automatic program distribution. We are building a research infrastructure that enables experimentation with various program partitioning and mapping strategies and the study of automatic distribution's effect on resource consumption (e.g., CPU, memory, communication). Since many optimization techniques are faced with conflicting optimization targets (e.g., memory and communication), we believe that it is important to be able to study their interaction. We present a set of techniques that enable flexible resource modeling and program distribution. These are: dependence analysis, weighted graph partitioning, code and communication generation, and profiling. We have developed these ideas in the context of the Java language. We present in detail the design and implementation of each of the techniques as part of our compiler and runtime infrastructure. Then, we evaluate our design and present preliminary experimental data for each component, as well as for the entire system

An input centric paradigm for program dynamic optimizations and lifetime evolvement

Accurately predicting program behaviors (e.g., memory locality, method calling frequency) is fundamental for program optimizations and runtime adaptations. Despite decades of remarkable progress, prior studies have not systematically exploited the use of program inputs, a deciding factor of program behaviors, to help in program dynamic optimizations. Triggered by the strong and predictive correlations between program inputs and program behaviors that recent studies have uncovered, the dissertation work aims to bring program inputs into the focus of program behavior analysis and program dynamic optimization, cultivating a new paradigm named input-centric program behavior analysis and dynamic optimization.;The new optimization paradigm consists of three components, forming a three-layer pyramid. at the base is program input characterization, a component for resolving the complexity in program raw inputs and extracting important features. In the middle is input-behavior modeling, a component for recognizing and modeling the correlations between characterized input features and program behaviors. These two components constitute input-centric program behavior analysis, which (ideally) is able to predict the large-scope behaviors of a program\u27s execution as soon as the execution starts. The top layer is input-centric adaptation, which capitalizes on the novel opportunities created by the first two components to facilitate proactive adaptation for program optimizations.;This dissertation aims to develop this paradigm in two stages. In the first stage, we concentrate on exploring the implications of program inputs for program behaviors and dynamic optimization. We construct the basic input-centric optimization framework based on of line training to realize the basic functionalities of the three major components of the paradigm. For the second stage, we focus on making the paradigm practical by addressing multi-facet issues in handling input complexities, transparent training data collection, predictive model evolvement across production runs. The techniques proposed in this stage together cultivate a lifelong continuous optimization scheme with cross-input adaptivity.;Fundamentally the new optimization paradigm provides a brand new solution for program dynamic optimization. The techniques proposed in the dissertation together resolve the adaptivity-proactivity dilemma that has been limiting the effectiveness of existing optimization techniques. its benefits are demonstrated through proactive dynamic optimizations in Jikes RVM and version selection using IBM XL C Compiler, yielding significant performance improvement on a set of Java and C/C++ programs. It may open new opportunities for a broad range of runtime optimizations and adaptations. The evaluation results on both Java and C/C++ applications demonstrate the new paradigm is promising in advancing the current state of program optimizations

Time-predictable Chip-Multiprocessor Design

Abstract—Real-time systems need time-predictable platforms to enable static worst-case execution time (WCET) analysis. Improving the processor performance with superscalar techniques makes static WCET analysis practically impossible. However, most real-time systems are multi-threaded applications and performance can be improved by using several processor cores on a single chip. In this paper we present a time-predictable chipmultiprocessor system that aims to improve system performance while still enabling WCET analysis. The proposed chip-multiprocessor (CMP) uses a shared memory with a time-division multiple access (TDMA) based memory access scheduling. The static TDMA schedule can be integrated into the WCET analysis. Experiments with a JOP based CMP showed that the memory access starts to dominate the execution time when using more than 4 processor cores. To provide a better scalability, more local memories have to be used. We add a processor local scratchpad memory and split data caches, which are still time-predictable, to the processor cores. I

Hybrid Caching for Chip Multiprocessors Using Compiler-Based Data Classification

The high performance delivered by modern computer system keeps scaling with an increasingnumber of processors connected using distributed network on-chip. As a result, memory accesslatency, largely dominated by remote data cache access and inter-processor communication, is becoming a critical performance bottleneck. To release this problem, it is necessary to localize data access as much as possible while keep efficient on-chip cache memory utilization. Achieving this however, is application dependent and needs a keen insight into the memory access characteristics of the applications. This thesis demonstrates how using fairly simple thus inexpensive compiler analysis memory accesses can be classified into private data access and shared data access. In addition, we introduce a third classification named probably private access and demonstrate the impact of this category compared to traditional private and shared memory classification. The memory access classification information from the compiler analysis is then provided to the runtime system through a modified memory allocator and page table to facilitate a hybrid private-shared caching technique. The hybrid cache mechanism is aware of different data access classification and adopts appropriate placement and search policies accordingly to improve performance. Our analysis demonstrates that many applications have a significant amount of both private and shared data and that compiler analysis can identify the private data effectively for many applications. Experimentsresults show that the implemented hybrid caching scheme achieves 4.03% performance improvement over state of the art NUCA-base caching

TreatJS: Higher-Order Contracts for JavaScript

TreatJS is a language embedded, higher-order contract system for JavaScript which enforces contracts by run-time monitoring. Beyond providing the standard abstractions for building higher-order contracts (base, function, and object contracts), TreatJS's novel contributions are its guarantee of non-interfering contract execution, its systematic approach to blame assignment, its support for contracts in the style of union and intersection types, and its notion of a parameterized contract scope, which is the building block for composable run-time generated contracts that generalize dependent function contracts. TreatJS is implemented as a library so that all aspects of a contract can be specified using the full JavaScript language. The library relies on JavaScript proxies to guarantee full interposition for contracts. It further exploits JavaScript's reflective features to run contracts in a sandbox environment, which guarantees that the execution of contract code does not modify the application state. No source code transformation or change in the JavaScript run-time system is required. The impact of contracts on execution speed is evaluated using the Google Octane benchmark.Comment: Technical Repor

A Survey on Thread-Level Speculation Techniques

Producción CientíficaThread-Level Speculation (TLS) is a promising technique that allows the parallel execution of sequential code without relying on a prior, compile-time-dependence analysis. In this work, we introduce the technique, present a taxonomy of TLS solutions, and summarize and put into perspective the most relevant advances in this field.MICINN (Spain) and ERDF program of the European Union: HomProg-HetSys project (TIN2014-58876-P), CAPAP-H5 network (TIN2014-53522-REDT), and COST Program Action IC1305: Network for Sustainable Ultrascale Computing (NESUS)