The SkipTrie: low-depth concurrent search without rebalancing

To date, all concurrent search structures that can support predecessor queries have had depth logarithmic in m, the number of elements. This paper introduces the SkipTrie, a new concurrent search structure supporting predecessor queries in amortized expected O(log log u + c) steps, insertions and deletions in O(c log log u), and using O(m) space, where u is the size of the key space and c is the contention during the recent past. The SkipTrie is a probabilistically-balanced version of a y-fast trie consisting of a very shallow skiplist from which randomly chosen elements are inserted into a hash-table based x-fast trie. By inserting keys into the x-fast-trie probabilistically, we eliminate the need for rebalancing, and can provide a lock-free linearizable implementation. To the best of our knowledge, our proof of the amortized expected performance of the SkipTrie is the first such proof for a tree-based data structure.National Science Foundation (U.S.) (Grant CCF-1217921)United States. Dept. of Energy. Office of Advanced Scientific Computing Research (Grant ER26116/DE-SC0008923)Oracle CorporationIntel Corporatio

Towards consistency oblivious programming

15th International Conference, OPODIS 2011, Toulouse, France, December 13-16, 2011. ProceedingsIt is well known that guaranteeing program consistency when accessing shared data comes at the price of degraded performance and scalability. This paper initiates the investigation of consistency oblivious programming (COP). In COP, sections of concurrent code that meet certain criteria are executed without checking for consistency. However, checkpoints are added before any shared data modification to verify the algorithm was on the right track, and if not, it is re-executed in a more conservative and expensive consistent way. We show empirically that the COP approach can enhance a software transactional memory (STM) framework to deliver more efficient concurrent data structures from serial source code. In some cases the COP code delivers performance comparable to that of more complex fine-grained structures

Lock cohorting: A general technique for designing NUMA locks

Multicore machines are quickly shifting to NUMA and CC-NUMA architectures, making scalable NUMA-aware locking algorithms, ones that take into account the machines' non-uniform memory and caching hierarchy, ever more important. This paper presents lock cohorting, a general new technique for designing NUMA-aware locks that is as simple as it is powerful. Lock cohorting allows one to transform any spin-lock algorithm, with minimal non-intrusive changes, into scalable NUMA-aware spin-locks. Our new cohorting technique allows us to easily create NUMA-aware versions of the TATAS-Backoff, CLH, MCS, and ticket locks, to name a few. Moreover, it allows us to derive a CLH-based cohort abortable lock, the first NUMA-aware queue lock to support abortability. We empirically compared the performance of cohort locks with prior NUMA-aware and classic NUMA-oblivious locks on a synthetic micro-benchmark, a real world key-value store application memcached, as well as the libc memory allocator. Our results demonstrate that cohort locks perform as well or better than known locks when the load is low and significantly out-perform them as the load increases

ThreadScan: Automatic and Scalable Memory Reclamation

The concurrent memory reclamation problem is that of devising a way for a deallocating thread to verify that no other concurrent threads hold references to a memory block being deallocated. To date, in the absence of automatic garbage collection, there is no satisfactory solution to this problem. Existing tracking methods like hazard pointers, reference counters, or epoch-based techniques like RCU, are either prohibitively expensive or require significant programming expertise, to the extent that implementing them efficiently can be worthy of a publication. None of the existing techniques are automatic or even semi-automated. In this paper, we take a new approach to concurrent memory reclamation: instead of manually tracking access to memory locations as done in techniques like hazard pointers, or restricting shared accesses to specific epoch boundaries as in RCU, our algorithm, called ThreadScan, leverages operating system signaling to automatically detect which memory locations are being accessed by concurrent threads. Initial empirical evidence shows that ThreadScan scales surprisingly well and requires negligible programming effort beyond the standard use of Malloc and Free

Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate

Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas. However, most state-of-the-art deep learning models either fail to obtain uncertainty estimation or need significant modification (e.g., formulating a proper Bayesian treatment) to obtain it. Most previous methods are not able to take an arbitrary model off the shelf and generate uncertainty estimation without retraining or redesigning it. To address this gap, we perform a systematic exploration into training-free uncertainty estimation for dense regression, an unrecognized yet important problem, and provide a theoretical construction justifying such estimations. We propose three simple and scalable methods to analyze the variance of outputs from a trained network under tolerable perturbations: infer-transformation, infer-noise, and infer-dropout. They operate solely during inference, without the need to re-train, re-design, or fine-tune the model, as typically required by state-of-the-art uncertainty estimation methods. Surprisingly, even without involving such perturbations in training, our methods produce comparable or even better uncertainty estimation when compared to training-required state-of-the-art methods.Comment: 18 pages, 13 figure

On the Inherent Sequentiality of Concurrent Objects

We present Ω(n) lower bounds on the worst case time to perform a single instance of an operation in any nonblocking implementation of a large class of concurrent data structures shared by n processes. Time is measured by the number of stalls a process incurs as a result of contention with other processes. For standard data structures such as counters, stacks, and queues, our bounds are tight. The implementations considered may apply any primitives to a base object. No upper bounds are assumed on either the number of base objects or their size

Interrupting snapshots and the Java[superscript TM] size method

The Java[superscript TM] developers kit requires a size() operation for all objects, tracking the number of elements in the object. Unfortunately, the best known solution, available in the Java concurrency package, has a blocking concurrent implementation that does not scale. This paper presents a highly scalable wait-free implementation of a concurrent size() operation based on a new lock-free interrupting snapshots algorithm. The key idea behind the new algorithm is to allow snapshot scan methods to interrupt each other until they agree on a shared linearization point with respect to update methods. This contrasts sharply with past approaches to the classical atomic snapshot problem, that have had threads coordinate the collecting of a shared global view. As we show empirically, the new algorithm scales well, significantly outperforming existing implementations.European Union (Project VELOX Grant FP7-ICT-2007-1

The big data challenges of connectomics

The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. However, modern connectomics produces 'big data', unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. Here we describe some of the key difficulties that may arise and provide suggestions for managing them.National Science Foundation (U.S.) (CCF-1217921)National Science Foundation (U.S.) (CCF-1301926)National Science Foundation (U.S.) (IIS-1447786)United States. Dept. of Energy (Advanced Scientific Computing Research Grant ER26116/DE-SC0008923)Oracle CorporationIntel Corporatio

The LevelArray: A Fast, Practical Long-Lived Renaming Algorithm

The long-lived renaming problem appears in shared-memory systems where a set of threads need to register and deregister frequently from the computation, while concurrent operations scan the set of currently registered threads. Instances of this problem show up in concurrent implementations of transactional memory, flat combining, thread barriers, and memory reclamation schemes for lock-free data structures. In this paper, we analyze a randomized solution for long-lived renaming. The algorithmic technique we consider, called the Level Array, has previously been used for hashing and one-shot (single-use) renaming. Our main contribution is to prove that, in long-lived executions, where processes may register and deregister polynomially many times, the technique guarantees constant steps on average and O (log log n) steps with high probability for registering, unit cost for deregistering, and O (n) steps for collect queries, where n is an upper bound on the number of processes that may be active at any point in time. We also show that the algorithm has the surprising property that it is self-healing: under reasonable assumptions on the schedule, operations running while the data structure is in a degraded state implicitly help the data structure re-balance itself. This subtle mechanism obviates the need for expensive periodic rebuilding procedures. Our benchmarks validate this approach, showing that, for typical use parameters, the average number of steps a process takes to register is less than two and the worst-case number of steps is bounded by six, even in executions with billions of operations. We contrast this with other randomized implementations, whose worst-case behavior we show to be unreliable, and with deterministic implementations, whose cost is linear in n.National Science Foundation (U.S.) (Grant CCF-1217921)National Science Foundation (U.S.) (Grant CCF-1301926)United States. Dept. of Energy (Advanced Scientific Computing Research Grant ER26116/DE-SC0008923)Oracle CorporationIntel Corporatio