Concurrent programming is notoriously di#cult. Current abstractions are intricate and make it hard to design computer systems that are reliable and scalable. We argue that these problems can be addressed by moving to a declarative style of concurrency control in which programmers directly indicate the safety properties that they require.

In this paper, we propose a new shared memory model: Transactional

This paper presents a novel interprocedural, flow-sensitive, and context-sensitive pointer analysis algorithm for multithreaded programs that may concurrently update shared pointers. For each pointer and each program point, the algorithm computes a conservative approximation of the memory locations to which that pointer may point. The algorithm correctly handles a full range of constructs in multithreaded programs, including recursive functions, function pointers, structures, arrays, nested structures and arrays, pointer arithmetic, casts between pointer variables of different types, heap and stack allocated memory, shared global variables, and thread-private global variables. We have implemented the algorithm in the SUIF compiler system and used the implementation to analyze a sizable set of multithreaded programs written in the Cilk multithreaded programming language. Our experimental results show that the analysis has good precision and converges quickly for our set of Cilk programs.

Many future shared-memory multiprocessor servers will both target commercial workloads and use highly-integrated "glueless" designs. Implementing low-latency cache coherence in these systems is difficult, because traditional approaches either add indirection for common cache-to-cache misses (directory protocols) or require a totally-ordered interconnect (traditional snooping protocols) . Unfortunately, totally-ordered interconnects are difficult to implement in glueless designs. An ideal coherence protocol would avoid indirections and interconnect ordering; however, such an approach introduces numerous protocol races that are difficult to resolve.

The literature on programming sensor networks has, by and large, focused on providing higher-level abstractions for expressing local node behavior. Kairos is a natural next step in sensor network programming in that it allows the programmer to express, in a centralized fashion, the desired global behavior of a distributed computation on the entire sensor network. Kairos’ compile-time and runtime subsystems expose a small set of programming primitives, while hiding from the programmer the details of distributed code generation and instantiation, remote data access and management, and inter-node program flow coordination. Kairos ’ runtime is greatly simplified by assuming eventual consistency in node state; this assumption underlies many practical distributed computations proposed for sensor networks. In this paper, we describe Kairos ’ programming model, and the flexibility and robustness it affords programmers. We demonstrate its suitability, through actual implementation, for a variety of distributed programs—both infrastructure services and signal processing tasks—typically encountered in sensor network literature: routing tree construction, localization, and object tracking. Our experimental results suggest that Kairos does not adversely affect the performance or accuracy of distributed programs, while our implementation experiences suggest that it greatly raises the level of abstraction presented to the programmer.

Transactional Coherence and Consistency (TCC) offers a way to simplify parallel programming by executing all code within transactions. In TCC systems, transactions serve as the fundamental unit of parallel work, communication and coherence. As each transaction completes, it writes all of its newly produced state to shared memory atomically, while restarting other processors that have speculatively read stale data. With this mechanism, a TCCbased system automatically handles data synchronization correctly, without programmer intervention. To gain the benefits of TCC, programs must be decomposed into transactions. We describe two basic programming language constructs for decomposing programs into transactions, a loop conversion syntax and a general transaction-forking mechanism. With these constructs, writing correct parallel programs requires only small, incremental changes to correct sequential programs. The performance of these programs may then easily be optimized, based on feedback from real program execution, using a few simple techniques.

Abstract — Transactional memory has great potential for simplifying multithreaded programming by allowing programmers to specify regions of the program that must appear to execute atomically. Transactional memory implementations then optimistically execute these transactions concurrently to obtain high performance. This work shows that the same atomic guarantees that give transactions their power also have unexpected and potentially serious negative effects on programs that were written assuming narrower scopes of atomicity. We make four contributions: (1) we show that a direct translation of lock-based critical sections into transactions can introduce deadlock into otherwise correct programs, (2) we introduce the terms strong atomicity and weak atomicity to describe the interaction of transactional and non-transactional code, (3) we show that code that is correct under weak atomicity can deadlock under strong atomicity, and (4) we demonstrate that sequentially composing transactional code can also introduce deadlocks. These observations invalidate the intuition that transactions are strictly safer than lock-based critical sections, that strong atomicity is strictly safer than weak atomicity, and that transactions are always composable. I.

Vector Microprocessors by Krste Asanovic Doctor of Philosophy in Computer Science University of California, Berkeley Professor John Wawrzynek, Chair Most previous research into vector architectures has concentrated on supercomputing applications and small enhancements to existing vector supercomputer implementations. This thesis expands the body of vector research by examining designs appropriate for single-chip full-custom vector microprocessor implementations targeting a much broader range of applications. I present the design, implementation, and evaluation of T0 (Torrent-0): the first single-chip vector microprocessor. T0 is a compact but highly parallel processor that can sustain over 24 operations per cycle while issuing only a single 32-bit instruction per cycle. T0 demonstrates that vector architectures are well suited to full-custom VLSI implementation and that they perform well on many multimedia and human-machine interface tasks. The remainder of the thesis contains ...

Currently multi-threaded C or C++ programs combine a single-threaded programming language with a separate threads library. This is not entirely sound [7]. We describe an effort, currently nearing completion, to address these issues by explicitly providing semantics for threads in the next revision of the C++ standard. Our approach is similar to that recently followed by Java [25], in that, at least for a well-defined and interesting subset of the language, we give sequentially consistent semantics to programs that do not contain data races. Nonetheless, a number of our decisions are often surprising even to those familiar with the Java effort: • We (mostly) insist on sequential consistency for race-free programs, in spite of implementation issues that came to light after the Java work. • We give no semantics to programs with data races. There are no benign C++ data races. • We use weaker semantics for trylock than existing languages or libraries, allowing us to promise sequential consistency with an intuitive race definition, even for programs with trylock. This paper describes the simple model we would like to be able to provide for C++ threads programmers, and explain how this, together with some practical, but often under-appreciated implementation constraints, drives us towards the above decisions.

Work on non-blocking data structures has proposed extending processor designs with a compare-and-swap primitive, CAS2, which acts on two arbitrary memory locations. Experience suggested that current operations, typically single-word compare-and-swap (CAS1), are not expressive enough to be used alone in an efficient manner. In this paper we build CAS2 from CAS1 and, in fact, build an arbitrary multi-word compare-and-swap (CASN). Our design requires only the primitives available on contemporary systems, reserves a small and constant amount of space in each word updated (either 0 or 2 bits) and permits nonoverlapping updates to occur concurrently. This provides compelling evidence that current primitives are not only universal in the theoretical sense introduced by Herlihy, but are also universal in their use as foundations for practical algorithms. This provides a straightforward mechanism for deploying many of the interesting non-blocking data structures presented in the literature that have previously required CAS2.