Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

The Art of Multiprocessor Programming

Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves. Reddit, the so called "front page of the Internet," in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and hundreds of billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically. In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.

The Pushshift Reddit Dataset

The VLDB journal : the international journal on very large data bases.

Most high-performance software transactional memories (STM) use optimistic invisible reads. Consequently, a transaction might have an inconsistent view of the objects it accesses unless the consistency of the view is validated whenever the view changes. Although all STMs usually detect inconsistencies at commit time, a transaction might never reach this point because an inconsistent view can provoke arbitrary behavior in the application (e.g., enter an infinite loop). In this paper, we formally introduce a lazy snapshot algorithm that verifies at each object access that the view observed by a transaction is consistent. Validating previously accessed objects is not necessary for that, however, it can be used on-demand to prolong the view's validity. We demonstrate both formally and by measurements that the performance of our approach is quite competitive by comparing other STMs with an STM that uses our algorithm.

https://doc.rero.ch/record/18139/files/Riegel_Torvald_-_A_Lazy_Snapshot_Algorithm_with_Eager_Validation_20100430.pdf

A Lazy Snapshot Algorithm with Eager Validation

Truffle is a Java-based framework for developing high-performance language runtimes. Language implementers aiming at developing new runtimes have to design all the runtime mechanisms for managing dynamically typed objects from scratch. This not only leads to potential code duplication, but also impacts the actual time needed to develop a fully-fledged runtime.In this paper we address this issue by introducing a common object storage model (OSM) for Truffle that can be used by language implementers to develop new runtimes. The OSM is generic, language-agnostic, and portable, as it can be used to implement a great variety of dynamic languages. It is extensible, featuring built-in support for custom extension mechanisms. It is also high-performance, as it is designed to benefit from the optimizing compiler in the Truffle framework. Our initial evaluation indicates that the Truffle OSM can be used to implement high-performance language runtimes, with no performance overhead when compared to language-specific solutions.

/pdf/an-object-storage-model-for-the-truffle-language-4z47yqutml.pdf

An object storage model for the truffle language implementation framework

Due to its popularity, there is an urgent need for dynamic program-analysis tools for Node.js, helping developers find bugs, performance bottlenecks, and bad coding practices. Frameworks based on code-level instrumentation enable dynamic analyses close to program semantics and are more flexible than Node.js built-in profiling tools. However, existing code-level instrumentation frameworks for JavaScript suffer from enormous overheads and difficulties in instrumenting the built-in module library of Node.js. In this paper, we introduce a new dynamic analysis framework for JavaScript and Node.js called NodeProf. While offering similar flexibility as code-level instrumentation frameworks, NodeProf significantly improves analysis performance while ensuring comprehensive code coverage. NodeProf supports runtime (de)activation of analyses and incurs zero overhead when no analysis is active. NodeProf is based on dynamic instrumentation of the JavaScript runtime and leverages automatic partial evaluation to generate efficient machine code. In addition, NodeProf makes use of the language interoperability provided by the runtime and thus allows dynamic analyses to be written in Java and JavaScript with compatibility to Jalangi, a state-of-the-art code-level JavaScript instrumentation framework. Our experiments show that the peak performance of running the same dynamic analyses using NodeProf can be up to three orders of magnitude faster than Jalangi.

Efficient dynamic analysis for Node.js

We are in the multi-core era. Dynamically-typed languages are in widespread use, but their support for multithreading still lags behind. One of the reasons is that the sophisticated techniques they use to efficiently represent their dynamic object models are often unsafe in multithreaded environments. This paper defines safety requirements for dynamic object models in multithreaded environments. Based on these requirements, a language-agnostic and thread-safe object model is designed that maintains the efficiency of sequential approaches. This is achieved by ensuring that field reads do not require synchronization and field updates only need to synchronize on objects shared between threads. Basing our work on JRuby+Truffle, we show that our safe object model has zero overhead on peak performance for thread-local objects and only 3% average overhead on parallel benchmarks where field updates require synchronization. Thus, it can be a foundation for safe and efficient multithreaded VMs for a wide range of dynamic languages.

/pdf/efficient-and-thread-safe-objects-for-dynamically-typed-2gvae6jsn2.pdf

Efficient and thread-safe objects for dynamically-typed languages

JSON is one of the most popular data encoding formats, with wide adoption in Databases and BigData frameworks as well as native support in popular programming languages such as JavaScript/Node.js, Python, and R.Nevertheless, JSON data processing can easily become a performance bottleneck in data-intensive applications because of parse and serialization overhead. In this paper, we introduce Fad.js, a runtime system for efficient processing of JSON objects in data-intensive applications. Fad.js is based on (1) speculative just-in-time (JIT) compilation and (2) selective access to data. Experiments show that applications using Fad.js achieve speedups up to 2.7x for encoding and 9.9x for decoding JSON data when compared to state-of-the art JSON processing libraries.

/pdf/fad-js-fast-json-data-access-using-jit-based-speculative-43k0ch05n0.pdf

FAD.js: fast JSON data access using JIT-based speculative optimizations

There is an urgent need for novel programming abstractions to leverage the parallelism in modern multicore machines. We introduce S, a new domain-specific language targeting the server-side scripting of high-performance RESTful Web services. S promotes an innovative programming model based on explicit (control-flow) and implicit (process-level) parallelism control, allowing the service developer to specify which portions of the control-flow should be executed in parallel. For each service, the choice of the best level of parallelism is left to the runtime system. We assess performance and scalability by implementing two non-trivial composite Web services in S. Experiments show that S-based Web services can handle thousands of concurrent client requests on a modern multicore machine.

/pdf/s-a-scripting-language-for-high-performance-restful-web-sfxxc3cijl.pdf

Daniele Bonetta

Papers

An object storage model for the truffle language implementation framework

Efficient dynamic analysis for Node.js

Efficient and thread-safe objects for dynamically-typed languages

FAD.js: fast JSON data access using JIT-based speculative optimizations

S: a scripting language for high-performance RESTful web services