Virtual Machine Support for Many-Core Architectures: Decoupling Abstract from Concrete Concurrency Models

The upcoming many-core architectures require software developers to exploit concurrency to utilize available computational power. Today's high-level language virtual machines (VMs), which are a cornerstone of software development, do not provide sufficient abstraction for concurrency concepts. We analyze concrete and abstract concurrency models and identify the challenges they impose for VMs. To provide sufficient concurrency support in VMs, we propose to integrate concurrency operations into VM instruction sets. Since there will always be VMs optimized for special purposes, our goal is to develop a methodology to design instruction sets with concurrency support. Therefore, we also propose a list of trade-offs that have to be investigated to advise the design of such instruction sets. As a first experiment, we implemented one instruction set extension for shared memory and one for non-shared memory concurrency. From our experimental results, we derived a list of requirements for a full-grown experimental environment for further research

Acute: high-level programming language design for distributed computation

Existing languages provide good support for typeful programming of standalone programs. In a distributed system, however, there may be interaction between multiple instances of many distinct programs, sharing some (but not necessarily all) of their module structure, and with some instances rebuilt with new versions of certain modules as time goes on. In this paper we discuss programming language support for such systems, focussing on their typing and naming issues. We describe an experimental language, Acute, which extends an ML core to support distributed development, deployment, and execution, allowing type-safe interaction between separately-built programs. The main features are: (1) type-safe marshalling of arbitrary values; (2) type names that are generated (freshly and by hashing) to ensure that type equality tests suffice to protect the invariants of abstract types, across the entire distributed system; (3) expression-level names generated to ensure that name equality tests suffice for type-safety of associated values, e.g. values carried on named channels; (4) controlled dynamic rebinding of marshalled values to local resources; and (5) thunkification of threads and mutexes to support computation mobility. These features are a large part of what is needed for typeful distributed programming. They are a relatively lightweight extension of ML, should be efficiently implementable, and are expressive enough to enable a wide variety of distributed infrastructure layers to be written as simple library code above the byte-string network and persistent store APIs. This disentangles the language runtime from communication intricacies. This paper highlights the main design choices in Acute. It is supported by a full language definition (of typing, compilation, and operational semantics), by a prototype implementation, and by example distribution libraries

Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

The moderate approach to integrating concurrency and object orientation

Principles of integrating concurrent computation, objects, and inheritance are discussed. The approach outlined here is guided by general considerations about the rôles of the two paradigms being merged. We suggest our approach to some open design questions: the relation of concurrency to inheritance and the thread and synchronisation concept among objects. The question, where should the combination of concurrency and objectorientation be settled between the paradigms, is analysed in different aspects. The presented approach also avoids the inheritance anomaly

Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs

Energy efficiency is becoming a pressing issue, especially in large data centers where it entails, at the same time, a non-negligible management cost, an enhancement of hardware fault probability, and a significant environmental footprint. In this paper, we study how Software Transactional Memories (STM) can provide benefits on both power saving and the overall applications’ execution performance. This is related to the fact that encapsulating shared-data accesses within transactions gives the freedom to the STM middleware to both ensure consistency and reduce the actual data contention, the latter having been shown to affect the overall power needed to complete the application’s execution. We have selected a set of self-adaptive extensions to existing STM middlewares (namely, TinySTM and R-STM) to prove how self-adapting computation can capture the actual degree of parallelism and/or logical contention on shared data in a better way, enhancing even more the intrinsic benefits provided by STM. Of course, this benefit comes at a cost, which is the actual execution time required by the proposed approaches to precisely tune the execution parameters for reducing power consumption and enhancing execution performance. Nevertheless, the results hereby provided show that adaptivity is a strictly necessary requirement to reduce energy consumption in STM systems: Without it, it is not possible to reach any acceptable level of energy efficiency at all