Code review is a crucial step in ensuring the quality and maintainability of software systems. However, this process can be time-consuming and resource-intensive, especially in large-scale projects where a significant number of code changes are submitted every day. Fortunately, not all code changes require human reviews, as some may only contain syntactic modifications that do not alter the behavior of the system, such as format changes, variable / function renamings, and constant extractions.

Microservices architecture, which is increasingly being adopted by large technology companies, can accelerate development and deployment of backend code by partitioning a monolithic infrastructure into independent components. At the same time, microservices often compose into massively distributed systems, where analyzing the behavior of an individual service may not be enough to diagnose a performance regression or find a point of failure. Instead, a more global view of the entire computation may be required, where some form of global context is used to trace relevant information flowing through the system.

The R language, from the point of view of language design and implementation, is a unique combination of various programming language concepts. It has functional characteristics like lazy evaluation of arguments, but also allows expressions to have arbitrary side effects. Many runtime data structures, for example variable scopes and functions, are accessible and can be modified while a program executes. Several different object models allow for structured programming, but the object models can interact in surprising ways with each other and with the base operations of R.

Today's Internet is long past static web pages filled with HTML-formatted text sprinkled with an occasional image or animation. We have entered an era of Rich Internet Applications executed locally on Internet clients such as web browsers: games, physics engines, image rendering, photo editing, etc. Yet today's languages used to program Internet clients have limited ability to tap to the computational capabilities of the underlying, often heterogeneous, platforms. In this paper we present how a Domain Specific Language(DSL) can be integrated into ActionScript, one of the most popular scripting languages used to program Internet clients and a close cousin of JavaScript. We demonstrate how our DSL, called ASDP (ActionScript Data Parallel), can be used to enable data parallelism for existing sequential programs. We also present a prototype of a system where data parallel workloads can be executed on either CPU or a GPU, with the runtime system transparently selecting the best processing unit, depending on the type of workload as well as the architecture and current load of the execution platform. We evaluate performance of our system on a variety of benchmarks, representing different types of workloads: physics, image processing, scientific computing and cryptography.

A number of deterministic parallel programming models with strong safety guarantees are emerging, but similar support for nondeterministic algorithms, such as branch and bound search, remains an open question. We present a language together with a type and effect system that supports nondeterministic computations with a deterministic-by-default guarantee: nondeterminism must be explicitly requested via special parallel constructs (marked nd), and any deterministic construct that does not execute any nd construct has deterministic input-output behavior. Moreover, deterministic parallel constructs are always equivalent to a sequential composition of their constituent tasks, even if they enclose, or are enclosed by, nd constructs. Finally, in the execution of nd constructs, interference may occur only between pairs of accesses guarded by atomic statements, so there are no data races, either between atomic statements and unguarded accesses (strong isolation) or between pairs of unguarded accesses (stronger than strong isolation alone). We enforce the guarantees at compile time with modular checking using novel extensions to a previously described effect system. Our effect system extensions also enable the compiler to remove unnecessary transactional synchronization. We provide a static semantics, dynamic semantics, and a complete proof of soundness for the language, both with and without the barrier removal feature. An experimental evaluation shows that our language can achieve good scalability for realistic parallel algorithms, and that the barrier removal techniques provide significant performance gains.

Memory transactions, similar to database transactions, allow a programmer to focus on the logic of their program and let the system ensure that transactions are atomic and isolated. Thus, programs using transactions do not suffer from deadlock. However, when a transaction performs I/O or accesses kernel resources, the atomicity and isolation guarantees from the TM system do not apply to the kernel.

We present the programming model, design and implementation of NePalTM; a transactional memory system where atomic blocks can be used for concurrency control at an arbitrary level of nested parallelism.

All in-text\treferences\tunderlined\tin\tblue\tare\tlinked\tto\tpublications\ton\tResearchGate, letting you\taccess\tand\tread\tthem\timmediately.

This paper presents a software transactional memory system that introduces first-class C++ language constructs for transactional programming. We describe new C++ language extensions, a production-quality optimizing C++ compiler that translates and optimizes these extensions, and a high-performance STM runtime library. The transactional language constructs support C++ language features including classes, inheritance, virtual functions, exception handling, and templates. The compiler automatically instruments the program for transactional execution and optimizes TM overheads. The runtime library implements multiple execution modes and implements a novel STM algorithm that supports both optimistic and pessimistic concurrency control. The runtime switches a transaction's execution mode dynamically to improve performance and to handle calls to precompiled functions and I/O libraries. We present experimental results on 8 cores (two quad-core CPUs) running a set of 20 non-trivial parallel programs. Our measurements show that our system scales well as the numbers of cores increases and that our compiler and runtime optimizations improve scalability.

As memory transactions have been proposed as a language-level replacement for locks, there is growing need for well-defined semantics. In contrast to database transactions, transaction memory (TM) semantics are complicated by the fact that programs may access the same memory locations both inside and outside transactions. Strongly atomic semantics, where non transactional accesses are treated as implicit single-operation transactions, remain difficult to provide without specialized hardware support or significant performance overhead. As an alternative, many in the community have informally proposed that a single global lock semantics [18,10], where transaction semantics are mapped to those of regions protected by a single global lock, provide an intuitive and efficiently implementable model for programmers.

Transactional memory (TM) provides a safer, more modular, and more scalable alternative to traditional lock-based synchronization. Implementing high performance TM systems has recently been an active area of research. However, current TM systems provide limited, if any, support for transactions executing irrevocable actions, such as I/O and system calls, whose side effects cannot in general be rolled back. This severely limits the ability of these systems to run commercial workloads.

Transactional Memory (TM) promises to simplify concurrent programming, which has been notoriously difficult but crucial in realizing the performance benefit of multi-core processors. Software Transaction Memory (STM), in particular, represents a body of important TM technologies since it provides a mechanism to run transactional programs when hardware TM support is not available, or when hardware TM resources are exhausted. Nonetheless, most previous researches on STMs were constrained to executing trivial, small-scale workloads. The assumption was that the same techniques applied to small-scale workloads could readily be applied to real-life, large-scale workloads. However, by executing several nontrivial workloads such as particle dynamics simulation and game physics engine on a state of the art STM, we noticed that this assumption does not hold. Specifically, we identified four major performance bottlenecks that were unique to the case of executing large-scale workloads on an STM: false conflicts, over-instrumentation, privatization-safety cost, and poor amortization. We believe that these bottlenecks would be common for any STM targeting real-world applications. In this paper, we describe those identified bottlenecks in detail, and we propose novel solutions to alleviate the issues. We also thoroughly validate these approaches with experimental results on real machines.

This paper proposes two approaches to managing concurrency in Java using a guarded region abstraction. Both approaches use revocation of such regions—the ability to undo their effects automatically and transparently. These new techniques alleviate many of the constraints that inhibit construction of transparently scalable and robust concurrent applications. The first solution, revocable monitors, augments existing mutual exclusion monitors with the ability to dynamically resolve priority inversion and deadlock, by reverting program execution to a consistent state when such situations are detected, while preserving Java semantics. The second technique, transactional monitors, extends the functionality of revocable monitors by implementing guarded regions as lightweight transactions that can be executed concurrently (or in parallel on multiprocessor platforms). The presentation includes discussion of design and implementation issues for both schemes, as well as a detailed performance study to compare their behavior with the traditional, state-of-the-art implementation of Java monitors based on mutual exclusion. Copyright © 2006 John Wiley & Sons, Ltd.

Virtual machines for languages such as the Java programming language make extensive use of online profiling and dynamic optimization to improve program performance. But despite the important role that profiling plays in achieving high performance, current virtual machines discard a program's profile data at the end of execution, wasting the opportunity to use past knowledge to improve future performance. In this paper, we present a fully automated architecture for exploiting cross-run profile data in virtual machines. Our work addresses a number of challenges that previously limited the practicality of such an approach.We apply this architecture to address the problem of selective optimization, and describe our implementation in IBM's J9 Java virtual machine. Our results demonstrate substantial performance improvements on a broad suite of Java programs, with the average performance ranging from 8.8% -- 16.6% depending on the execution scenario.

A future is a simple and elegant abstraction that allows concurrency to be expressed often through a relatively small rewrite of a sequential program. In the absence of side-effects, futures serve as benign annotations that mark potentially concurrent regions of code. Unfortunately, when computation relies heavily on mutation as is the case in Java, its meaning is less clear, and much of its intended simplicity lost.This paper explores the definition and implementation of safe futures for Java. One can think of safe futures as truly transparent annotations on method calls, which designate opportunities for concurrency. Serial programs can be made concurrent simply by replacing standard method calls with future invocations. Most significantly, even though some parts of the program are executed concurrently and may indeed operate on shared data, the semblance of serial execution is nonetheless preserved. Thus, program reasoning is simplified since data dependencies present in a sequential program are not violated in a version augmented with safe futures.Besides presenting a programming model and API for safe futures, we formalize the safety conditions that must be satisfied to ensure equivalence between a sequential Java program and its future-annotated counterpart. A detailed implementation study is also provided. Our implementation exploits techniques such as object versioning and task revocation to guarantee necessary safety conditions. We also present an extensive experimental evaluation of our implementation to quantify overheads and limitations. Our experiments indicate that for programs with modest mutation rates on shared data, applications can use futures to profitably exploit parallelism, without sacrificing safety.

Priority inversion occurs in concurrent programs when low-priority threads hold shared resources needed by some high-priority thread, causing them to block indefinitely. Shared resources are usually guarded by low-level synchronization primitives such as mutual-exclusion locks, semaphores, or monitors. There are two existing solutions to priority inversion. The first, establishing high-level scheduling invariants over synchronization primitives to eliminate priority inversion a priori, is difficult in practice and undecidable in general. Alternatively, run-time avoidance mechanisms such as priority inheritance still force high-priority threads to wait until desired resources are released. We describe a novel compiler and run-time solution to the problem of priority inversion, along with experimental evaluation of its effectiveness. Our approach allows preemption of any thread holding a resource needed by higher-priority threads, forcing it to release its claim on the resource, roll back its execution to the point at which the shared resource was first acquired, and discard any updates made in the interim. The compiler inserts code at synchronization points, permitting rollback of thread execution, and efficient revocation of interim updates. Our design and implementation are realized in the context of IBM's Jikes RVM, a high-quality compiler and runtime system for Java. Our performance results show that throughput of high-priority threads using our scheme can be improved by 30% to 100% when compared with a classical scheduler that does not address priority inversion.