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Open-domain question answering aims to get answers for given questions from a set of documents. Recently, dual encoder architecture is widely adopted to dense passage retrieval for question answering. In-batch negative sampling is typically used to gather extra negative samples during training. In this paper, we propose adaptive batch scheduling to enhance the performance of in-batch negative sampling. The proposed algorithm schedules training batches to increase the difficulty of the sampled negatives by in-batch negative sampling during training. We evaluated the proposed approach on the two well-known document retrieval benchmark datasets MSMARCO and Natural Questions. The evaluation result shows that the proposed adaptive batch scheduling could significantly improve the document retrieval performances of dual encoder architecture document retrieval systems.

In this paper, we focus on the problem of optimizing deadline violations for executing tasks in various heterogeneous computational environments. To address the problem, we formulated it as a binary nonlinear programming (BNP) model, which maximize the number of completed tasks and optimize the resource utilization of servers. To solve the BNP model in a polynomial complexity, we propose a heuristic task scheduling method, which iteratively schedules a task to the first core such that the accumulated slack time of all scheduled tasks is minimum, until the core cannot finish any task, and executes tasks with the earliest deadline first in each core to execute as many task as possible in a core. Experiment results based on a real world trace show that our method has upto 100% less task violations, and has the best performance in resource efficiency optimization in overall, compared with eight classical and state-of-the-art heuristic methods.

Recently, Shabtay considered a scheduling problem on a single serial-batching machine with rejection to minimize the dual criteria of total completion time and total rejection cost, where the number of jobs to be included in each batch is not restricted. He studied four variants of the problem: the first is to minimize the sum of the two criteria; the second and third are to minimize one criterion, subject to the other criterion not exceeding a given value; and the last is to find the Pareto-optimal solutions for the bicriterion problem. Shabtay provided an O ( \textitn^\mathbf 5 ) algorithm for the first variant and an O ( n ^\mathbf 6/\boldsymbol \varepsilon ^\mathbf 2 ) fully polynomial-time approximation scheme (FPTAS) for the fourth variant. In this paper, we provide an alternative O ( n ^\mathbf 4 ) algorithm to solve the first variant and an O ( n ^\mathbf 5/\boldsymbol \varepsilon ) FPTAS for the fourth variant, which are more efficient than those developed by Shabtay from a theoretical perspective. However, when the size of each batch is bounded by a given number b~\boldsymbol >1 , the corresponding time complexities of our algorithms for the first and fourth variants reduce to O (bn ^\mathbf 3 ) and O (bn ^\mathbf 4/\boldsymbol \varepsilon ), respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a three-stage buffered Clos-network switch (TSBCS) architecture along with a novel batch scheduling (BS) mechanism. We found that TSBCS/BS can be mapped to a “fat” combined input-crosspoint queued (CICQ) switch. Consequently, the well-studied CICQ scheduling algorithms can be directly applied in TSBCS. Moreover, BS drastically reduces the time complexity of TSBCS scheduling when compared with ordinary CICQ switches of the same number of switch ports, which enables us to build a larger-capacity switch with reasonable scheduling complexity. We further show that TSBCS/BS can achieve 100 percent throughput under any admissible traffic if a stable CICQ scheduling algorithm is used. Direct cell forwarding schemes are proposed to overcome the performance drawback of BS under light traffic loads. With extensive simulations, we show that the performance of TSBCS/BS is comparable to that of output-queued switches and the latter are usually considered as theoretical optimal. [ABSTRACT FROM AUTHOR]

Batch scheduling accommodates a group of tasks with the start/end time constraints to maximize the revenue from scheduling tasks over a number of servers, which has been extensively studied in the context of job?machine scheduling. In optical networks, batch scheduling refers to the process of scheduling a group of data units (i.e., the jobs) competing for the same set of wavelength channels (i.e., the machines). Classical job?machine scheduling studies have considered both the case of a pure-loss system, and the case with waiting rooms (i.e., buffers), which are generally in the form of random access memory (RAM). In optical networks, the buffering is achieved by feeding the optical signal into a fixed length of fiber, namely, a fiber delay line (FDL), since optical RAM is not yet available. The unique feature of the discrete and predefined buffering time in fact instantiates a new type of problem, namely, job?machine scheduling with discrete-time buffers. In this work, we comprehensively study batch scheduling in optical networks. We show that batch scheduling with and without FDLs corresponds to two different instances of the job?machine scheduling problem. While proving their NP-completeness, we mathematically model both cases using integer linear programming formulations to provide an optimal scheduling. Given the timeliness request for on-line batch scheduling and the dramatic problem size in optical networks, we also propose polynomial-time heuristic algorithms, which are shown to be near optimal in our simulations. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of grouping different customer orders into jobs and these formed jobs to batches and scheduling them on a single batch processing machine to minimize the total weighted tardiness of orders. A compatible batching environment is considered in which there is no restriction on the orders being from the same family to be batched together. A mixed-integer program (MIP) is developed and tested. The compatible MIP model takes 1 + h, on average, to solve an instance. A new simulated annealing-based heuristic solves compatible problems to within 4% of optimality in a few minutes. This fast, near optimal performance suggests that these heuristics may be viable for implementation in practical manufacturing settings, such as burn-in operations in the back end of the semiconductor manufacturing process [ABSTRACT FROM PUBLISHER]

We propose a novel job scheduling approach for homogeneous cluster computing platforms. Its key feature is the use of virtual machine technology to share fractional node resources in a precise and controlled manner. Other VM-based scheduling approaches have focused primarily on technical issues or extensions to existing batch scheduling systems, while we take a more aggressive approach and seek to find heuristics that maximize an objective metric correlated with job performance. We derive absolute performance bounds and develop algorithms for the online nonclairvoyant version of our scheduling problem. We further evaluate these algorithms in simulation against both synthetic and real-world HPC workloads and compare our algorithms to standard batch scheduling approaches. We find that our approach improves over batch scheduling by orders of magnitude in terms of job stretch, while leading to comparable or better resource utilization. Our results demonstrate that virtualization technology coupled with lightweight online scheduling strategies can afford dramatic improvements in performance for executing HPC workloads. [ABSTRACT FROM AUTHOR]