A methodology for creating fast wait-free data structures

Kogan, Alex; Petrank, Erez

doi:10.1145/2145816.2145835

Cited by 66 publications

(55 citation statements)

References 23 publications

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“…However, the process of cleaning up the set at thread termination (see below) requires wait-freedom as other threads may still be accessing the data structure. Helping approaches can be used to (even efficiently [19]) solve this problem. Experiments suggest that contention on these sets is low and we thus keep the implementation simple.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…However, unlike segment queues [1] and k-Stacks [15], Distributed Queues [11] with Treiber stacks [29], as used in scalloc, do not require dynamically allocated administrative memory (such as sentinel nodes), which is important for building efficient memory allocators. The data structures for reusable spans within a TLAB are implemented using locks but could in principle be replaced with wait-free sets, which nowadays can be implemented almost as efficiently as their lock-free counterparts [19].…”

Section: Related Workmentioning

confidence: 99%

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Fast, multicore-scalable, low-fragmentation memory allocation through large virtual memory and global data structures

et al. 2015

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We demonstrate that general-purpose memory allocation involving many threads on many cores can be done with high performance, multicore scalability, and low memory consumption. For this purpose, we have designed and implemented scalloc, a concurrent allocator that generally performs and scales in our experiments better than other allocators while using less memory, and is still competitive otherwise. The main ideas behind the design of scalloc are: uniform treatment of small and big objects through so-called virtual spans, efficiently and effectively reclaiming free memory through fast and scalable global data structures, and constant-time (modulo synchronization) allocation and deallocation operations that trade off memory reuse and spatial locality without being subject to false sharing.

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Section: Implementation Detailsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fast, multicore-scalable, low-fragmentation memory allocation through large virtual memory and global data structures

et al. 2015

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“…An alternative bounded variant has also been proposed by Tsigas and Zhang (TZqueue) [92], which uses a slightly different mechanism but performs similarly due to its use of contended CAS for committing operations. It also serves as the basis for a variety of later lock-free queues including the wait-free queues presented by Kogan and Petrank [55,56], which offer even stronger progress guarantees, but with a cost that increases with each additional thread. Queues like these are also common components of relaxed queues [47,54].…”

Section: Concurrent Data Structuresmentioning

confidence: 99%

“…It has gone through a variety of forms -from infinite-array queues [45,50] to lock-free queues [69,92] to advanced distributed lock-free [47,54] or even wait-free [55,56] variants. As concurrency has increased, so has the contention on concurrent queues and the cost of synchronization, and frequently serialization, in these designs.…”

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confidence: 99%

Runtime Adaptation for Autonomic Heterogeneous Computing

Scogland

Feng

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity. I have also had the good fortune to collaborate with both Lawrence Livermore National Laboratory and Argonne National Laboratory. Experiences as an intern at each lab has had a significant effect on the final shape of my dissertation. Special thanks go to Dr. Pavan Balaji (again), Dr. Bronis de Supinski (again) and Dr. Barry Rountree. These collaborations proved to be major turning points for me, both in my research and my life as a whole.

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“…It has gone through a variety of forms -from infinite-array queues [7,4] to lock-free queues [11,14] to advanced distributed lock-free [8,5] or even wait-free [9,10] variants. As concurrency has increased, so has the contention on concurrent queues and the cost of synchronization, and frequently serialization, in these designs.…”

mentioning

confidence: 99%

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

Scogland

Feng

2015

Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

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As core counts increase and as heterogeneity becomes more common in parallel computing, we face the prospect of programming hundreds or even thousands of concurrent threads in a single shared-memory system. At these scales, even highly-efficient concurrent algorithms and data structures can become bottlenecks, unless they are designed from the ground up with throughput as their primary goal.In this paper, we present three contributions: (1) a characterization of queue designs in terms of modern multi-and many-core architectures, (2) the design of a high-throughput, linearizable, blocking, concurrent FIFO queue for many-core architectures that avoids the bottlenecks and pitfalls common in modern queue designs, and (3) a thorough evaluation of concurrent queue throughput across CPU, GPU, and co-processor devices. Our evaluation shows that focusing on throughput, rather than progress guarantees, allows our queue to scale to as much as three orders of magnitude (1000×) faster than lock-free and combining queues on GPU platforms and two times (2×) faster on CPU devices. These results deliver critical insights into the design of data structures for highly concurrent systems: (1) progress guarantees do not guarantee scalability, and (2) allowing an algorithm to block can increase throughput.

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A methodology for creating fast wait-free data structures

Cited by 66 publications

References 23 publications

Fast, multicore-scalable, low-fragmentation memory allocation through large virtual memory and global data structures

Fast, multicore-scalable, low-fragmentation memory allocation through large virtual memory and global data structures

Runtime Adaptation for Autonomic Heterogeneous Computing

Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures

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