Implementation and Performance of Munin

Munin is a distributed shared memory (DSM) system that allows shared memory paral­lel programs to be executed efficiently on distributed memory multiprocessors. Munin is unique among existing DSM systems in its use of multiple consistency protocols and in its use of release consistency. In Munin, shared program variables are annotated with their expected access pattern, and these annotations are then used by the runtime system to choose a consistency protocol best suited to that access pattern. Release consistency allows Munin to mask network latency and reduce the number of messages required to keep memory consistent. Munin's multi­protocol release consistency is implemented in software using a delayed update queue that buffers and merges pending outgoing writes. A sixteen­processor prototype of Munin is currently operational. We evaluate its imple­ mentation and describe the execution of two Munin programs that achieve performance within ten percent of message passing implementations of the same programs. Munin achieves this level of performance with only minor annotations to the shared memory programs

Case studies in symbolic model checking

technical reportThe need to formally verify hardware and software systems before they are deployed the real world has been recognized for several decades now. This is especially true of concurrent systems that are even more difficult to debug than sequential systems. For example, many of the protocols that get employed in real-life systems often look deceptively simple at first glance, and yet often contain hidden errors. In many cases, these errors cannot be revealed through simulation alone. Tools that are powerful enough to verify real-life systems of significant sizes have, however, only recently become available, thanks to developments such as efficient Boolean reasoning methods. As a result, there is a real opportunity amongst practitioners of formal methods to apply these tools to real-life examples and to teach tomorrow's designers?today's students?the use of these tools for solving practically significant problems. This paper is about our efforts in this regard. Among formal verification tools that can verify concurrent systems, model checking tools have become quite popular. There are several reasons for this. First, their ability to carry out proofs with relatively very little human intervention makes it possible for designers to automatically carry out many of the proofs, thus freeing them up for more creative tasks. Second, most concurrent hardware and software systems are one-ofs, and hence it is very difficult to recoup human effort put into one project for use in another

An Analysis of Distributed Systems Syllabi With a Focus on Performance-Related Topics

We analyze a dataset of 51 current (2019-2020) Distributed Systems syllabi from top Computer Science programs, focusing on finding the prevalence and context in which topics related to performance are being taught in these courses. We also study the scale of the infrastructure mentioned in DS courses, from small client-server systems to cloud-scale, peer-to-peer, global-scale systems. We make eight main findings, covering goals such as performance, and scalability and its variant elasticity; activities such as performance benchmarking and monitoring; eight selected performance-enhancing techniques (replication, caching, sharding, load balancing, scheduling, streaming, migrating, and offloading); and control issues such as trade-offs that include performance and performance variability.Comment: Accepted for publication at WEPPE 2021, to be held in conjunction with ACM/SPEC ICPE 2021: https://doi.org/10.1145/3447545.3451197 This article is a follow-up of our prior ACM SIGCSE publication, arXiv:2012.0055

Evaluating the potential of programmable multiprocessor cache controllers

technical reportThe next generation of scalable parallel systems (e.g., machines by KSR, Convex, and others) will have shared memory supported in hardware, unlike most current generation machines (e.g., offerings by Intel, nCube, and Thinking Machines). However, current shared memory architectures are constrained by the fact that their cache controllers are hardwired and inflexible, which limits the range of programs that can achieve scalable performance. This observation has led a number of researchers to propose building programmable multiprocessor cache controllers that can implement a variety of caching protocols, support multiple communication paradigms, or accept guidance from software. To evaluate the potential performance benefits of these designs, we have simulated five SPLASH benchmark programs on a virtual multiprocessor that supports five directory-based caching protocols. When we compared the off-line optimal performance of this design, wherein each cache line was maintained using the protocol that required the least communication, with the performance achieved when using a single protocol for all lines, we found that use of the "optimal" protocol reduced consistency traffic by 10-80%, with a mean improvement of 25-35%. Cache miss rates also dropped by up to 25%. Thus, the combination of programmable (or tunable) hardware and software able to exploit this added flexibility, e.g., via user pragmas or compiler analysis, could dramatically improve the performance of future shared memory multiprocessors

Java on Networks of Workstations (JavaNOW): A Parallel Computing Framework Inspired by Linda and the Message Passing Interface (MPI)

Networks of workstations are a dominant force in the distributed computing arena, due primarily to the excellent price/performance ratio of such systems when compared to traditionally massively parallel architectures. It is therefore critical to develop programming languages and environments that can help harness the raw computational power available on these systems. In this article, we present JavaNOW (Java on Networks of Workstations), a Java‐based framework for parallel programming on networks of workstations. It creates a virtual parallel machine similar to the MPI (Message Passing Interface) model, and provides distributed associative shared memory similar to the Linda memory model but with a richer set of primitive operations. JavaNOW provides a simple yet powerful framework for performing computation on networks of workstations. In addition to the Linda memory model, it provides for shared objects, implicit multithreading, implicit synchronization, object dataflow, and collective communications similar to those defined in MPI. JavaNOW is also a component of the Computational Neighborhood, a Java‐enabled suite of services for desktop computational sharing. The intent of JavaNOW is to present an environment for parallel computing that is both expressive and reliable and ultimately can deliver good to excellent performance. As JavaNOW is a work in progress, this article emphasizes the expressive potential of the JavaNOW environment and presents preliminary performance results only

Parallel processing for scientific computations

The scope of this project dealt with the investigation of the requirements to support distributed computing of scientific computations over a cluster of cooperative workstations. Various experiments on computations for the solution of simultaneous linear equations were performed in the early phase of the project to gain experience in the general nature and requirements of scientific applications. A specification of a distributed integrated computing environment, DICE, based on a distributed shared memory communication paradigm has been developed and evaluated. The distributed shared memory model facilitates porting existing parallel algorithms that have been designed for shared memory multiprocessor systems to the new environment. The potential of this new environment is to provide supercomputing capability through the utilization of the aggregate power of workstations cooperating in a cluster interconnected via a local area network. Workstations, generally, do not have the computing power to tackle complex scientific applications, making them primarily useful for visualization, data reduction, and filtering as far as complex scientific applications are concerned. There is a tremendous amount of computing power that is left unused in a network of workstations. Very often a workstation is simply sitting idle on a desk. A set of tools can be developed to take advantage of this potential computing power to create a platform suitable for large scientific computations. The integration of several workstations into a logical cluster of distributed, cooperative, computing stations presents an alternative to shared memory multiprocessor systems. In this project we designed and evaluated such a system