Dynamic Defragmentation of Reconfigurable Devices

Fekete, Sándor P.; Kamphans, Tom; Schweer, Nils; Tessars, Christopher; Veen, Jan C. van der; Angermeier, Josef; Koch, Dirk; Teich, Jürgen

doi:10.1145/2209285.2209287

“…The goal is to minimize the number of changes to the allocation. Other papers that solve specific instances of reallocation problems include [15,17,19,28,32].…”

Section: Other Related Workmentioning

confidence: 99%

“…In our own research, we have modelled FPGA reorganization as a reallocation problem, minimizing reconfiguration costs [17]. We have also explored reallocation problems in database resource management with respect to crash safety and transactional support [8].…”

Section: Introductionmentioning

confidence: 99%

Cost-Oblivious Reallocation for Scheduling and Planning

Bender

¹

,

Farach-Colton

²

,

Fekete

³

et al. 2015

Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and Architectures

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In a reallocating-scheduler problem, jobs may be inserted and deleted from the system over time. Unlike in traditional online scheduling problems, where a job's placement is immutable, in reallocation problems the schedule may be adjusted, but at some cost. The goal is to maintain an approximately optimal schedule while also minimizing the reallocation cost for changing the schedule.This paper gives a reallocating scheduler for the problem of assigning jobs to p (identical) servers so as to minimize the sum of completion times to within a constant factor of optimal, with an amortized reallocation cost for a lengthw job of O(f (w) · log 3 log ∆), where ∆ is the length of the longest job and f () is the reallocation-cost function. Our algorithm is cost oblivious, meaning that the algorithm is not parameterized by f (), yet it achieves this bound for any subadditive f (). Whenever f () is strongly subadditive, the reallocation cost becomes O(f (w)).To realize a reallocating scheduler with low reallocation cost, we design a k-cursor sparse table. This data structure stores a dynamic set of elements in an array, with insertions and deletions restricted to k "cursors" in the structure. The data structure achieves an amortized cost of O(log 3 k) for insertions and deletions, while also guaranteeing that any prefix of the array has constant density. Observe that this bound does not depend on n, the number of elements, and hence this data structure, restricted to k n cursors, beats the lower bound of Ω(log 2 n) for general sparse tables.

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“…Reallocation is a natural problem. Many existing algorithms, when looked in the right way, can be viewed as reallocation problems, e.g., reconfiguring FPGAs [14], maintaining a sparse array [9,17,[31][32][33], or maintaining an on-line topological ordering (e.g., [8,15,21]). We believe that the framework developed in this paper will allow us to achieve new insights into classical scheduling and optimization problems and the cost of changing a good solution when circumstances change.…”

Section: Reallocation Problemsmentioning

confidence: 99%

Reallocation Problems in Scheduling

Bender

¹

,

Farach-Colton

²

,

Fekete

³

et al. 2014

Self Cite

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In traditional on-line problems, such as scheduling, requests arrive over time, demanding available resources. As each request arrives, some resources may have to be irrevocably committed to servicing that request. In many situations, however, it may be possible or even necessary to reallocate previously allocated resources in order to satisfy a new request. This reallocation has a cost. This paper shows how to service the requests while minimizing the reallocation cost.We focus on the classic problem of scheduling jobs on a multiprocessor system. Each unit-size job has a time window in which it can be executed. Jobs are dynamically added and removed from the system. We provide an algorithm that maintains a valid schedule, as long as a sufficiently feasible schedule exists. The algorithm reschedules only O(min{log * n, log * ∆}) jobs for each job that is inserted or deleted from the system, where n is the number of active jobs and ∆ is the size of the largest window.

show abstract

“…Reallocation is a natural problem. Many existing algorithms, when looked in the right way, can be viewed as reallocation problems, e.g., reconfiguring FPGAs [14], maintaining a sparse array [9,17,[31][32][33], or maintaining an on-line topological ordering (e.g., [8,15,21]). We believe that the framework developed in this paper will allow us to achieve new insights into classical scheduling and optimization problems and the cost of changing a good solution when circumstances change.…”

Section: Reallocation Problemsmentioning

confidence: 99%

Reallocation problems in scheduling

Bender

¹

,

Farach-Colton

²

,

Fekete

³

et al. 2013

Proceedings of the Twenty-Fifth Annual ACM Symposium on Parallelism in Algorithms and Architectures

View full text Add to dashboard Cite

In traditional on-line problems, such as scheduling, requests arrive over time, demanding available resources. As each request arrives, some resources may have to be irrevocably committed to servicing that request. In many situations, however, it may be possible or even necessary to reallocate previously allocated resources in order to satisfy a new request. This reallocation has a cost. This paper shows how to service the requests while minimizing the reallocation cost.We focus on the classic problem of scheduling jobs on a multiprocessor system. Each unit-size job has a time window in which it can be executed. Jobs are dynamically added and removed from the system. We provide an algorithm that maintains a valid schedule, as long as a sufficiently feasible schedule exists. The algorithm reschedules only O(min{log * n, log * ∆}) jobs for each job that is inserted or deleted from the system, where n is the number of active jobs and ∆ is the size of the largest window.

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Dynamic Defragmentation of Reconfigurable Devices

Cited by 11 publications

References 22 publications

Cost-Oblivious Reallocation for Scheduling and Planning

Cost-Oblivious Reallocation for Scheduling and Planning

Reallocation Problems in Scheduling

Reallocation problems in scheduling

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