A Comparison of Relativistic and Reader-Writer Locking Approaches to Shared Data Access

This paper explores the relationship between reader-writer locking and relativistic programming approaches to managing accesses to shared data. It demonstrates that by placing certain restrictions on writers, relativistic programming allows more concurrency than reader-writer locking while still providing the same isolation guarantees. Relativistic programming also allows for a straightforward model for reasoning about the correctness of programs that allow concurrent read-write accesses

Resizable, Scalable, Concurrent Hash Tables

We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow the hash table to maintain constant-time performance as the number of entries grows, and reclaim memory as the number of entries decreases, without delaying or disrupting readers. We implemented our algorithms in the Linux kernel, to test their performance and scalability. Benchmarks show lookup scalability improved 125x over readerwriter locking, and 56% over the current state-of-the-art for Linux, with no performance degradation for lookups during a resize. To achieve this performance, this hash table implementation uses a new concurrent programming methodology known as relativistic programming. In particular, we make use of an existing synchronization primitive which waits for all current readers to finish, with little to no reader overhead; careful use of this primitive allows ordering of updates without read-side synchronization or memory barriers

Asynchronized Concurrency: The Secret to Scaling Concurrent Search Data Structures

We introduce "asynchronized concurrency (ASCY),'' a paradigm consisting of four complementary programming patterns. ASCY calls for the design of concurrent search data structures (CSDSs) to resemble that of their sequential counterparts. We argue that ASCY leads to implementations which are portably scalable: they scale across different types of hardware platforms, including single and multi-socket ones, for various classes of workloads, such as read-only and read-write, and according to different performance metrics, including throughput, latency, and energy. We substantiate our thesis through the most exhaustive evaluation of CSDSs to date, involving 6 platforms, 22 state-of-the-art CSDS algorithms, 10 re-engineered state-of-the-art CSDS algorithms following the ASCY patterns, and 2 new CSDS algorithms designed with ASCY in mind. We observe up to 30% improvements in throughput in the re-engineered algorithms, while our new algorithms out-perform the state-of-the-art alternatives

Scalable Concurrent Hash Tables via Relativistic Programming

Existing approaches to concurrent programming often fail to account for synchronization costs on modern shared-memory multipro- cessor architectures. A new approach to concurrent programming, known as relativistic programming, can reduce or in some cases eliminate synchronization overhead on such architectures. This approach avoids the costs of inter-processor communication and memory access by permitting processors to operate from a relativistic view of memory provided by their own caches, rather than from an absolute reference frame of memory as seen by all processors. This research shows how relativistic programming techniques can provide the perceived advantages of optimistic synchronization without the useless parallelism caused by rollbacks and retries. Descriptions of several implementations of a concurrent hash table illustrate the differences between traditional and relativistic approaches to concurrent programming. An analysis of the fundamental performance bottlenecks in existing concurrent programming techniques, both optimistic and pessimistic, directly motivates the key ideas of relativistic programming. Relativistic techniques provide performance and scalability advantages over traditional synchronization, demonstrated through benchmarks of concurrent hash tables implemented in the Linux kernel. The demonstrated relativistic hash table makes use of an original relativistic hash table move operation. The paper concludes with a discussion of how the specific techniques used in the concurrent hash table implementation generalize to other data structures and algorithms

Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures

High-performance programs and systems require concurrency to take full advantage of available hardware. However, the available concurrent programming models force a difficult choice, between simple models such as mutual exclusion that produce little to no concurrency, or complex models such as Read-Copy Update that can scale to all available resources. Simple concurrent programming models enforce atomicity and causality, and this enforcement limits concurrency. Scalable concurrent programming models expose the weakly ordered hardware memory model, requiring careful and explicit enforcement of causality to preserve correctness, as demonstrated in this dissertation through the manual construction of a scalable hash-table item-move algorithm. Recent research on relativistic programming aims to standardize the programming model of Read-Copy Update, but thus far these efforts have lacked a generalized memory ordering model, requiring data-structure-specific reasoning to preserve causality. I propose a new memory ordering model, relativistic causal ordering , which combines the scalabilty of relativistic programming and Read-Copy Update with the simplicity of reader atomicity and automatic enforcement of causality. Programs written for the relativistic model translate to scalable concurrent programs for weakly-ordered hardware via a mechanical process of inserting barrier operations according to well-defined rules. To demonstrate the relativistic causal ordering model, I walk through the straightforward construction of a novel concurrent hash-table resize algorithm, including the translation of this algorithm from the relativistic model to a hardware memory model, and show through benchmarks that the resulting algorithm scales far better than those based on mutual exclusion

Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures

High-performance programs and systems require concurrency to take full advantage of available hardware. However, the available concurrent programming models force a difficult choice, between simple models such as mutual exclusion that produce little to no concurrency, or complex models such as Read-Copy Update that can scale to all available resources. Simple concurrent programming models enforce atomicity and causality, and this enforcement limits concurrency. Scalable concurrent programming models expose the weakly ordered hardware memory model, requiring careful and explicit enforcement of causality to preserve correctness, as demonstrated in this dissertation through the manual construction of a scalable hash-table item-move algorithm. Recent research on relativistic programming aims to standardize the programming model of Read-Copy Update, but thus far these efforts have lacked a generalized memory ordering model, requiring data-structure-specific reasoning to preserve causality. I propose a new memory ordering model, relativistic causal ordering , which combines the scalabilty of relativistic programming and Read-Copy Update with the simplicty of reader atomicity and automatic enforcement of causality. Programs written for the relativistic model translate to scalable concurrent programs for weakly-ordered hardware via a mechanical process of inserting barrier operations according to well-defined rules. To demonstrate the relativistic causal ordering model, I walk through the straightforward construction of a novel concurrent hash-table resize algorithm, including the translation of this algorithm from the relativistic model to a hardware memory model, and show through benchmarks that the resulting algorithm scales far better than those based on mutual exclusion

The Ordering Requirements of Relativistic and Reader-Writer Locking Approaches to Shared Data Access

The semantics of reader-writer locks allow read-side concurrency. Unfortunately, the locking primitives serialize access to the lock variable to an extent that little or no concurrency is realized in practice for small critical sections. Relativistic programming is a methodology that also allows read- side concurrency. Relativistic programming uses dfferent ordering constraints than reader-writer locking. The different ordering constraints allow relativistic readers to proceed without synchronization so relativistic readers scale even for very short critical sections. In this paper we explore the diferences between the ordering constraints for reader-writer locking and relativistic programs. We show how and why the dfferent ordering constraints allow relativistic programs to have both better performance and better scalability than their reader-writer locking counterparts

Scalable Correct Memory Ordering via Relativistic Programming

We propose and document a new concurrent programming model, relativistic programming. This model allows readers to run concurrently with writers, without blocking or using expensive synchronization. Relativistic programming builds on existing synchronization primitives that allow writers to wait for current readers to finish with minimal reader overhead. Our methodology models data structures as graphs, and reader algorithms as traversals of these graphs; from this foundation we show how writers can implement arbitrarily strong ordering guarantees for the visibility of their writes, up to and including total ordering

Generalized Construction of Scalable Concurrent Data Structures via Relativistic Programming

We present relativistic programming, a concurrent programming model based on shared addressing, which supports efficient, scalable operation on either uniform shared-memory or distributed shared- memory systems. Relativistic programming provides a strong causal ordering property, allowing a series of read operations to appear as an atomic transaction that occurs entirely between two ordered write operations. This preserves the simple immutable-memory programming model available via mutual exclusion or transactional memory. Furthermore, relativistic programming provides joint-access parallelism, allowing readers to run concurrently with a writer on the same data. We demonstrate a generalized construction technique for concurrent data structures based on relativistic programming, taking into account the natural orderings provided by reader traversals and writer program order. Our construction technique specifies the precise placement of memory barriers and synchronization operations. To demonstrate our generalized approach, we reconstruct the algorithms for existing relativistic data structures, replacing the algorithm-specific reasoning with our systematic and rigorous construction. Benchmarks of the resulting relativistic read algorithms demonstrate high performance and linear scalability: relativistic resizable hash-tables demonstrate 56% better lookup performance than the current state of the art in Linux, and relativistic red-black trees show 2.5x better lookup performance than transactional memory