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Modern programming languages offer special syntax and semantics for logical fork-join parallelism in the form of parallel loops, allowing them to be nested, e.g., a parallel loop within another parallel loop. This expressiveness comes at a price, however: on modern multicore systems, realizing logical parallelism results in overheads due to the creation and management of parallel tasks, which can wipe out the benefits of parallelism. Today, we expect application programmers to cope with it by manually tuning and optimizing their code. Such tuning requires programmers to reason about architectural factors hidden behind layers of software abstractions, such as task scheduling and load balancing. Managing these factors is particularly challenging when workloads are irregular because their performance is input-sensitive. This paper presents HBC, the first compiler that translates C/C++ programs with high-level, fork-join constructs (e.g., OpenMP) to binaries capable of automatically controlling the cost of parallelism and dealing with irregular, input-sensitive workloads. The basis of our approach is Heartbeat Scheduling, a recent proposal for automatic granularity control, which is backed by formal guarantees on performance. HBC binaries outperform OpenMP binaries for workloads for which even entirely manual solutions struggle to find the right balance between parallelism and its costs. 

Recent work has proposed a memory property for parallel programs, called disentanglement, and showed that it is pervasive in a variety of programs, written in different languages, ranging from C/C++ to Parallel ML, and showed that it can be exploited to improve the performance of parallel functional programs. All existing work on disentanglement, however, considers the fork/join model for parallelism and does not apply to futures, the more powerful approach to parallelism. This is not surprising: fork/join parallel programs exhibit a reasonably strict dependency structure (e.g., series-parallel DAGs), which disentanglement exploits. In contrast, with futures, parallel computations become first-class values of the language, and thus can be created, and passed between functions calls or stored in memory, just like other ordinary values, resulting in complex dependency structures, especially in the presence of mutable state. For example, parallel programs with futures can have deadlocks, which is impossible with fork-join parallelism.

In this paper, we are interested in the theoretical question of whether disentanglement may be extended beyond fork/join parallelism, and specifically to futures. We consider a functional language with futures, Input/Output (I/O), and mutable state (references) and show that a broad range of programs written in this language are disentangled. We start by formalizing disentanglement for futures and proving that purely functional programs written in this language are disentangled. We then generalize this result in three directions. First, we consider state (effects) and prove that stateful programs are disentangled if they are race free. Second, we show that race freedom is sufficient but not a necessary condition and non-deterministic programs, e.g. those that use atomic read-modify-operations and some non-deterministic combinators, may also be disentangled. Third, we prove that disentangled task-parallel programs written with futures are free of deadlocks, which arise due to interactions between state and the rich dependencies that can be expressed with futures. Taken together, these results show that disentanglement generalizes to parallel programs with futures and, thus, the benefits of disentanglement may go well beyond fork-join parallelism.

 

On any modern computer architecture today, parallelism comes with a modest cost, born from the creation and management of threads or tasks. Today, programmers battle this cost by manually optimizing/tuning their codes to minimize the cost of parallelism without harming its benefit, performance. This is a difficult battle: programmers must reason about architectural constant factors hidden behind layers of software abstractions, including thread schedulers and memory managers, and their impact on performance, also at scale. In languages that support higher-order functions, the battle hardens: higher order functions can make it difficult, if not impossible, to reason about the cost and benefits of parallelism.

Motivated by these challenges and the numerous advantages of high-level languages, we believe that it has become essential to manage parallelism automatically so as to minimize its cost and maximize its benefit. This is a challenging problem, even when considered on a case-by-case, application-specific basis. But if a solution were possible, then it could combine the many correctness benefits of high-level languages with performance by managing parallelism without the programmer effort needed to ensure performance. This paper proposes techniques for such automatic management of parallelism by combining static (compilation) and run-time techniques. Specifically, we consider the Parallel ML language with task parallelism, and describe a compiler pipeline that embeds potential parallelism directly into the call-stack and avoids the cost of task creation by default. We then pair this compilation pipeline with a run-time system that dynamically converts potential parallelism into actual parallel tasks. Together, the compiler and run-time system guarantee that the cost of parallelism remains low without losing its benefit. We prove that our techniques have no asymptotic impact on the work and span of parallel programs and thus preserve their asymptotic properties. We implement the proposed techniques by extending the MPL compiler for Parallel ML and show that it can eliminate the burden of manual optimization while delivering good practical performance.

 

Although functional programming languages simplify writing safe parallel programs by helping programmers to avoid data races, they have traditionally delivered poor performance. Recent work improved performance by using a hierarchical memory architecture that allows processors to allocate and reclaim memory independently without any synchronization, solving thus the key performance challenge afflicting functional programs. The approach, however, restricts mutation, or memory effects, so as to ensure "disentanglement", a low-level memory property that guarantees independence between different heaps in the hierarchy. This paper proposes techniques for supporting entanglement and for allowing functional programs to use mutation at will. Our techniques manage entanglement by distinguishing between disentangled and entangled objects and shielding disentangled objects from the cost of entanglement management. We present a semantics that formalizes entanglement as a property at the granularity of memory objects, and define several cost metrics to reason about and bound the time and space cost of entanglement. We present an implementation of the techniques by extending the MPL compiler for Parallel ML. The extended compiler supports all features of the Parallel ML language, including unrestricted effects. Our experiments using a variety of benchmarks show that MPL incurs a small time and space overhead compared to sequential runs, scales well, and is competitive with languages such as C++, Go, Java, OCaml. These results show that our techniques can marry the safety benefits of functional programming with performance. 

Many concurrent programs assign priorities to threads to improve responsiveness. When used in conjunction with synchronization mechanisms such as mutexes and condition variables, however, priorities can lead to priority inversions, in which high-priority threads are delayed by low-priority ones. Priority inversions in the use of mutexes are easily handled using dynamic techniques such as priority inheritance, but priority inversions in the use of condition variables are not well-studied and dynamic techniques are not suitable. In this work, we use a combination of static and dynamic techniques to prevent priority inversion in code that uses mutexes and condition variables. A type system ensures that condition variables are used safely, even while dynamic techniques change thread priorities at runtime to eliminate priority inversions in the use of mutexes. We prove the soundness of our system, using a model of priority inversions based on cost models for parallel programs. To show that the type system is practical to implement, we encode it within the type systems of Rust and C++, and show that the restrictions are not overly burdensome by writing sizeable case studies using these encodings, including porting the Memcached object server to use our C++ implementation. 

Recent research on parallel functional programming has culminated in a provably efficient (in work and space) parallel memory manager, which has been incorporated into the MPL (MaPLe) compiler for Parallel ML and shown to deliver practical efficiency and scalability. The memory manager exploits a property of parallel programs called disentanglement, which restricts computations from accessing concurrently allocated objects. Disentanglement is closely related to race-freedom, but subtly differs from it. Unlike race-freedom, however, no known techniques exists for ensuring disentanglement, leaving the task entirely to the programmer. This is a challenging task, because it requires reasoning about low-level memory operations (e.g., allocations and accesses), which is especially difficult in functional languages. In this paper, we present techniques for detecting entanglement dynamically, while the program is running. We first present a dynamic semantics for a functional language with references that checks for entanglement by consulting parallel and sequential dependency relations in the program. Notably, the semantics requires checks for mutable objects only. We prove the soundness of the dynamic semantics and present several techniques for realizing it efficiently, in particular by pruning away a large number of entanglement checks. We also provide bounds on the work and space of our techniques. We show that the entanglement detection techniques are practical by implementing them in the MPL compiler for Parallel ML. Considering a variety of benchmarks, we present an evaluation and measure time and space overheads of less than 5% on average with up to 72 cores. These results show that entanglement detection has negligible cost and can therefore remain deployed with little or no impact on efficiency, scalability, and space. 

High-level parallel languages (HLPLs) make it easier to write correct parallel programs. Disciplined memory usage in these languages enables new optimizations for hardware bottlenecks, such as cache coherence. In this work, we show how to reduce the costs of cache coherence by integrating the hardware coherence protocol directly with the programming language; no programmer effort or static analysis is required. We identify a new low-level memory property, WARD (WAW Apathy and RAW Dependence-freedom), by construction in HLPL programs. We design a new coherence protocol, WARDen, to selectively disable coherence using WARD. We evaluate WARDen with a widely-used HLPL benchmark suite on both current and future x64 machine structures. WARDen both accelerates the benchmarks (by an average of 1.46x) and reduces energy (by 23%) by eliminating unnecessary data movement and coherency messages. 

Because of its many desirable properties, such as its ability to control effects and thus potentially disastrous race conditions, functional programming offers a viable approach to programming modern multicore computers. Over the past decade several parallel functional languages, typically based on dialects of ML and Haskell, have been developed. These languages, however, have traditionally underperformed procedural languages (such as C and Java). The primary reason for this is their hunger for memory, which only grows with parallelism, causing traditional memory management techniques to buckle under increased demand for memory. Recent work opened a new angle of attack on this problem by identifying a memory property of determinacy-race-free parallel programs, called disentanglement, which limits the knowledge of concurrent computations about each other’s memory allocations. The work has showed some promise in delivering good time scalability. In this paper, we present provably space-efficient automatic memory management techniques for determinacy-race-free functional parallel programs, allowing both pure and imperative programs where memory may be destructively updated. We prove that for a program with sequential live memory of R * , any P -processor garbage-collected parallel run requires at most O ( R * · P ) memory. We also prove a work bound of O ( W + R * P ) for P -processor executions, accounting also for the cost of garbage collection. To achieve these results, we integrate thread scheduling with memory management. The idea is to coordinate memory allocation and garbage collection with thread scheduling decisions so that each processor can allocate memory without synchronization and independently collect a portion of memory by consulting a collection policy, which we formulate. The collection policy is fully distributed and does not require communicating with other processors. We show that the approach is practical by implementing it as an extension to the MPL compiler for Parallel ML. Our experimental results confirm our theoretical bounds and show that the techniques perform and scale well.