The Art of Multiprocessor Programming

Stephen J. Hartley Oxford University Press, New York, 1998, 260 pp. ISBN 0-19-511315-2, $45.00 Concurrent Programming  is a thorough treatment of Java multi-threaded programming for both a stand-alone and distributed environment. Designed mostly for students in concurrent or parallel programming classes, the text is also an excellent reference for the practicing professional developing multi-threaded programs or applets. Hartley first provides a complete explanation of the features of Java necessary to write concurrent programs, including topics such as exception handling, interfaces, and packages. He then gives the reader a solid background to write multi-threaded programs and also presents the problems introduced when writing concurrent programs—namely race conditions, mutual exclusion, and deadlock. Hartley also provides several software solutions that do not require the use of common process and thread mechanisms. Once the groundwork is laid for writing concurrent programs, Hartley then takes a different approach than most Java references. Rather than presenting how Java handles mutual exclusion with the  synchronized  keyword (although it is covered later), he first looks at semaphore-based solutions to classic concurrent problems such as bounded-buffer, readers-writers, and the dining philosophers. Hartley also uses the same approach to develop Java classes for monitors and message passing. This unique approach to introducing concurrency allows the readers to both understand how Java threads are synchronized and how the basic synchronization mechanism can be used to construct more abstract tools such as semaphores. If there is a shortcoming with the text it is with the lack of sufficient coverage of remote method invocation (RMI), although there is a section covering RMI. This is quite understandable as RMI is a fairly recent phenomenon with the Java community. Also, the classes that Hartley provides could easily implement RMI rather than sockets to handle communication. The strengths of the book include its ease in reading, several examples at the end of chapters, a package similar to Xtango that provides algorithm animation, and a supportive web site by the author (see  www.mcs.drexel.edu/~shartley/ConcProgJava/index.html ) including compressed source code. As Java becomes more dominant on the server side of multi-tier applications, writing thread-safe concurrent applications becomes even more important.  Concurrent Programming  is a strong step towards teaching students and professionals such skills. Greg Gagne, Westminster College of Salt Lake City Salt Lake City, Utah

Distributed Computing: Fundamentals, Simulations and Advanced Topics

An overview on smart contracts : Challenges, advances and platforms

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

Blockchain is the promising technology of recent years, which has attracted remarkable attention in both academic studies and practical industrial applications. The smart contract is a programmable transaction that can perform a sophisticated task, execute automatically, and store on the blockchain. The smart contract is the key component of the blockchain, which has made blockchain a technology beyond the scope of the cryptocurrencies and applicable for a variety of applications such as healthcare, IoT, supply chain, digital identity, business process management, and more. Although in recent years the progress toward improving blockchain technology with the focus on the smart contract has been impressive, there is a lack of reviewing the smart contract topic. This paper systematically reviews the key concepts and proposes the direction of recent studies and developments regarding the smart contract. The research studies are presented in three main categories: 1) security methods and tools; 2) performance improvement approaches; and 3) decentralized applications based on smart contracts.

Security, Performance, and Applications of Smart Contracts: A Systematic Survey

Software Transactional Memory Systems STM are a promising alternative for concurrency control in shared memory systems. Multiversion STM systems maintain multiple versions for each t-object. The advantage of storing multiple versions is that it facilitates successful execution of higher number of read operations than otherwise. Multi-Version permissiveness mv-permissiveness is a progress condition for multi-version STMs that states that a read-only transaction never aborts. Recently a STM system was proposed that maintains only a single version but is mv-permissive. This raises a natural question: how much concurrency can be achieved by multi-version STM. We show that fewer transactions are aborted in multi-version STMs than single-version systems. We also show that any STM system that is permissive w.r.t opacity must maintain at least as many versions as the number of live transactions. A direct implication of this result is that no single-version STM can be permissive w.r.t opacity.

In this paper we present a time-stamp based multiversion STM system that satisfies opacity and is easy to implement. We formally prove the correctness of the proposed STM system. Although many multi-version STM systems have been proposed in literature that satisfy opacity, to the best of our knowledge none of them has been formally proved to be opaque. We also present garbage collection procedure which deletes unwanted versions of the transaction objects. We show that with garbage collection the number of versions maintained is bounded by number of live transactions.

/pdf/a-timestamp-based-multi-version-stm-algorithm-uucyoejs0o.pdf

A TimeStamp Based Multi-version STM Algorithm

Blockchain platforms such as Ethereum and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. Normally, a block of the chain consists of multiple transactions of smart contracts which are added by a miner. To append a correct block into the blockchain, miners execute these transactions of smart contracts sequentially. Later the validators serially re-execute the smart contract transactions of the block. If the validators agree with the final state of the block as recorded by the miner, then the block is said to be validated. It is then added to the blockchain using a consensus protocol. In Ethereum and other blockchains that support cryptocurrencies, a miner gets an incentive every time such a valid block successfully added to the blockchain. In most of the current day blockchains the miners and validators execute the smart contract transactions serially. In the current era of multi-core processors, by employing the serial execution of the transactions, the miners and validators fail to utilize the cores properly and as a result, have poor throughput. By adding concurrency to smart contracts execution, we can achieve better efficiency and higher throughput. In this paper, we develop an efficient framework to execute the smart contract transactions concurrently using optimistic Software Transactional Memory systems (STMs). Miners execute smart contract transactions concurrently using multi-threading to generate the final state of blockchain. STM is used to take care of synchronization issues among the transactions and ensure atomicity. Now when the validators also execute the transactions (as a part of validation) concurrently using multi-threading, then the validators may get a different final state depending on the order of execution of conflicting transactions. To avoid this, the miners also generate a block graph of the transactions during the concurrent execution and store it in the block. This graph captures the conflict relations among the transactions and is generated concurrently as the transactions are executed by different threads. The miner proposes a block which consists of set of transactions, block graph, hash of the previous block, and final state of each shared data-objects. Later, the validators re-execute the same smart contract transactions concurrently and deterministically with the help of block graph given by the miner to verify the final state. If the validation is successful then proposed block appended into the blockchain and miner gets incentive otherwise discard the proposed block. We execute the smart contract transactions concurrently using Basic Time stamp Ordering (BTO) and Multi-Version Time stamp Ordering (MVTO) protocols as optimistic STMs. BTO and MVTO miner achieves 3.6x and 3.7x average speedups over serial miner respectively. Along with, BTO and MVTO validator outperform average 40.8x and 47.1x than serial validator respectively.

An Efficient Framework for Optimistic Concurrent Execution of Smart Contracts

The group mutual exclusion problem extends the traditional mutual exclusion problem by associating a type (or a group) with each critical section. In this problem, processes requesting critical sections of the same type can execute their critical sections concurrently. However, processes requesting critical sections of different types must execute their critical sections in a mutually exclusive manner. We present a distributed algorithm for solving the group mutual exclusion problem based on the notion of surrogate-quorum. Intuitively, our algorithm uses the quorum that has been successfully locked by a request as a surrogate to service other compatible requests for the same type of critical section. Unlike the existing quorum-based algorithms for group mutual exclusion, our algorithm achieves a low message complexity of O(q) and a low (amortized) bit-message complexity of O(bqr), where q is the maximum size of a quorum, b is the maximum number of processes from which a node can receive critical section requests, and r is the maximum size of a request while maintaining both synchronization delay and waiting time at two message hops. As opposed to some existing quorum-based algorithms, our algorithm can adapt without performance penalties to dynamic changes in the set of groups. Our simulation results indicate that our algorithm outperforms the existing quorum-based algorithms for group mutual exclusion by as much as 45 percent in some cases. We also discuss how our algorithm can be extended to satisfy certain desirable properties such as concurrent entry and unnecessary blocking freedom.

/pdf/a-quorum-based-group-mutual-exclusion-algorithm-for-a-1g9yd0iytp.pdf

A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set

Non-interference and local correctness in transactional memory☆☆☆

Detecting termination of a distributed computation is a fundamental problem in distributed systems We present two optimal algorithms for detecting termination of a non-diffusing distributed computation for an arbitrary topology Both algorithms are optimal in terms of message complexity and detection latency The first termination detection algorithm has to be initiated along with the underlying computation The message complexity of this algorithm is Θ(N+M) and its detection latency is Θ(D), where N is the number of processes in the system, M is the number of application messages exchanged by the underlying computation, and D is the diameter of the communication topology The second termination detection algorithm can be initiated at any time after the underlying computation has started The message complexity of this algorithm is Θ(E+M) and its detection latency is Θ(D), where E is the number of channels in the communication topology

Sathya Peri

Papers

A TimeStamp Based Multi-version STM Algorithm

An Efficient Framework for Optimistic Concurrent Execution of Smart Contracts

A Quorum-Based Group Mutual Exclusion Algorithm for a Distributed System with Dynamic Group Set

Non-interference and local correctness in transactional memory☆☆☆

Message-optimal and latency-optimal termination detection algorithms for arbitrary topologies