Callbacks are essential in many programming environments, but drastically complicate program understanding and reasoning because they allow to mutate object's local states by external objects in unexpected fashions, thus breaking modularity. The famous DAO bug in the cryptocurrency framework Ethereum, employed callbacks to steal $150M. We define the notion of Effectively Callback Free (ECF) objects in order to allow callbacks without preventing modular reasoning.An object is ECF in a given execution trace if there exists an equivalent execution trace without callbacks to this object. An object is ECF if it is ECF in every possible execution trace. We study the decidability of dynamically checking ECF in a given execution trace and statically checking if an object is ECF. We also show that dynamically checking ECF in Ethereum is feasible and can be done online. By running the history of all execution traces in Ethereum, we were able to verify that virtually all existing contract executions, excluding these of the DAO or of contracts with similar known vulnerabilities, are ECF. Finally, we show that ECF, whether it is verified dynamically or statically, enables modular reasoning about objects with encapsulated state.

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Log-structured data stores (LSM-DSs) are widely accepted as the state-of-the-art implementation of key-value stores. They replace random disk writes with sequential I/O, by accumulating large batches of updates in an in-memory data structure and merging it with the on-disk store in the background. While LSM-DS implementations proved to be highly successful at masking the I/O bottleneck, scaling them up on multicore CPUs remains a challenge. This is nontrivial due to their often rich APIs, as well as the need to coordinate the RAM access with the background I/O.We present cLSM, an algorithm for scalable concurrency in LSM-DS, which exploits multiprocessor-friendly data structures and non-blocking synchronization. cLSM supports a rich API, including consistent snapshot scans and general non-blocking read-modify-write operations.We implement cLSM based on the popular LevelDB keyvalue store, and evaluate it using intensive synthetic workloads as well as ones from production web-serving applications. Our algorithm outperforms state of the art LSM-DS implementations, improving throughput by 1.5x to 2.5x. Moreover, cLSM demonstrates superior scalability with the number of cores (successfully exploiting twice as many cores as the competition).

In this article, we consider concurrent programs in which the shared state consists of instances of linearizable abstract data types (ADTs). We present an automated approach to concurrency control that addresses a common need: the need to atomically execute a code fragment, which may contain multiple ADT operations on multiple ADT instances. We present a synthesis algorithm that automatically enforces atomicity of given code fragments (in a client program) by inserting pessimistic synchronization that guarantees atomicity and deadlock-freedom (without using any rollback mechanism). Our algorithm takes a commutativity specification as an extra input. This specification indicates for every pair of ADT operations the conditions under which the operations commute. Our algorithm enables greater parallelism by permitting commuting operations to execute concurrently. We have implemented the synthesis algorithm in a Java compiler and applied it to several Java programs. Our results show that our approach produces efficient and scalable synchronization.

Linearizable libraries provide operations that appear to execute atomically. Clients, however, may need to execute a sequence of operations (a composite operation) atomically. We consider the problem of extending a linearizable library to support arbitrary atomic composite operations by clients. We introduce a novel approach in which the concurrent library ensures atomicity of composite operations by exploiting information (foresight) provided by its clients. We use a correctness condition, based on a notion of dynamic rightmovers, that guarantees that composite operations execute atomically without deadlocks, and without using rollbacks.We present a static analysis to infer the foresight information required by our approach, allowing a compiler to automatically insert the foresight information into the client. This relieves the client programmer of this burden and simplifies writing client code.We present a generic technique for extending the library implementation to realize foresight-based synchronization. This technique is used to implement a general-purpose Java library for Map data structures -the library permits composite operations to simultaneously work with multiple instances of Map data structures.We use the Maps library and the static analysis to enforce atomicity of a wide selection of real-life Java composite operations. Our experiments indicate that our approach enables realizing efficient and scalable synchronization for real-life composite operations.