arXiv subjects

Wei Quan Lim

Publications and source records attributed to Wei Quan Lim.

5 recordsLinked to original sources

arXiv2021

Optimal Multithreaded Batch-Parallel 2-3 Trees

This paper presents a batch-parallel 2-3 tree T in an asynchronous dynamic multithreading model that supports searches, insertions and deletions in sorted batches and has essentially optimal parallelism, even under the restrictive QRMW (queued read-modify-write) memory contention model where concurrent accesses to the same memory location are queued and serviced one by one. Specifically, if T has n items, then performing an item-sorted batch (given as a leaf-based balanced binary tree) of b operations on T takes O( b * log(n/b+1) + b ) work and O( log b + log n ) span (in the worst case as b,n -> inf). This is information-theoretically work-optimal for b <= n, and also span-optimal for pointer-based structures. Moreover, it is easy to support optimal intersection, union and difference of instances of T with sizes m <= n, namely within O( m * log(n/m+1) ) work and O( log m + log n ) span. Furthermore, T supports other batch operations that make it a very useful building block for parallel data structures. To the author's knowledge, T is the first parallel sorted-set data structure that can be used in an asynchronous multi-processor machine under a memory model with queued contention and yet have asymptotically optimal work and span. In fact, T is designed to have bounded contention and satisfy the claimed work and span bounds regardless of the execution schedule. Since all data structures and algorithms in this paper fit into the dynamic multithreading paradigm, all their performance bounds are directly composable with those of other data structures and algorithms in the same model. Finally, the pipelining techniques in this paper are also likely to be very useful in asynchronous parallelization of other recursive data structures.

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arXiv2019

Small Connected Planar Graph with 1-Cop-Move Number 4

This paper describes a 720-vertex connected planar graph G such that cop1(G), denoting the minimum number of cops needed to catch the robber in the 1-cop-move game on G, is at least 4 and at most 7. Furthermore, G has a connected subgraph H such that cop1(H) is exactly 4, meaning that 4 cops are barely sufficient to catch the robber in the 1-cop-move game on H. This is a significant improvement over the graph given by Gao and Yang in 2017.

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arXiv2019

Parallel Finger Search Structures

In this paper we present two versions of a parallel finger structure FS on p processors that supports searches, insertions and deletions, and has a finger at each end. This is to our knowledge the first implementation of a parallel search structure that is work-optimal with respect to the finger bound and yet has very good parallelism (within a factor of O( (log p)^2 ) of optimal). We utilize an extended implicit batching framework that transparently facilitates the use of FS by any parallel program P that is modelled by a dynamically generated DAG D where each node is either a unit-time instruction or a call to FS. The total work done by either version of FS is bounded by the finger bound F[L] (for some linearization L of D ), i.e. each operation on an item with finger distance r takes O( log r + 1 ) amortized work; it is cheaper for items closer to a finger. Running P using the simpler version takes O( ( T[1] + F[L] ) / p + T[inf] + d * ( (log p)^2 + log n ) ) time on a greedy scheduler, where T[1],T[inf] are the size and span of D respectively, and n is the maximum number of items in FS, and d is the maximum number of calls to FS along any path in D. Using the faster version, this is reduced to O( ( T[1] + F[L] ) / p + T[inf] + d * (log p)^2 + s[L] ) time, where s[L] is the weighted span of D where each call to FS is weighted by its cost according to F[L]. We also sketch how to extend FS to support a fixed number of movable fingers. The data structures in our paper fit into the dynamic multithreading paradigm, and their performance bounds are directly composable with other data structures given in the same paradigm. Also, the results can be translated to practical implementations using work-stealing schedulers.

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arXiv2018

Parallel Working-Set Search Structures

In this paper we present two versions of a parallel working-set map on p processors that supports searches, insertions and deletions. In both versions, the total work of all operations when the map has size at least p is bounded by the working-set bound, i.e., the cost of an item depends on how recently it was accessed (for some linearization): accessing an item in the map with recency r takes O(1+log r) work. In the simpler version each map operation has O((log p)^2+log n) span (where n is the maximum size of the map). In the pipelined version each map operation on an item with recency r has O((log p)^2+log r) span. (Operations in parallel may have overlapping span; span is additive only for operations in sequence.) Both data structures are designed to be used by a dynamic multithreading parallel program that at each step executes a unit-time instruction or makes a data structure call. To achieve the stated bounds, the pipelined data structure requires a weak-priority scheduler, which supports a limited form of 2-level prioritization. At the end we explain how the results translate to practical implementations using work-stealing schedulers. To the best of our knowledge, this is the first parallel implementation of a self-adjusting search structure where the cost of an operation adapts to the access sequence. A corollary of the working-set bound is that it achieves work static optimality: the total work is bounded by the access costs in an optimal static search tree.

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arXiv2016

Dynamic Reallocation Problems in Scheduling

In this paper we look at the problem of scheduling tasks on a single-processor system, where each task requires unit time and must be scheduled within a certain time window, and each task can be added to or removed from the system at any time. On each operation, the system is allowed to reschedule any tasks, but the goal is to minimize the number of rescheduled tasks. Our main result is an allocator that maintains a valid schedule for all tasks in the system if their time windows have constant size and reschedules O(1/ε*log(1/ε)) tasks on each insertion as ε->0, where ε is a certain measure of the schedule flexibility of the system. We also show that it is optimal for any allocator that works on arbitrary instances. We also briefly mention a few variants of the problem, such as if the tasks have time windows of difference sizes, for which we have an allocator that we conjecture reschedules only 1 task on each insertion if the schedule flexibility remains above a certain threshold.

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