A polynomial-time custom instruction identification algorithm based on dynamic programming

2011 ◽  
Author(s):  
Junwhan Ahn ◽  
Imyong Lee ◽  
Kiyoung Choi
Keyword(s):  
Dynamic Programming ◽  
Polynomial Time ◽  
Identification Algorithm ◽  
Custom Instruction ◽  
Instruction Identification

scholarly journals Polynomial Time Instances for the IKHO Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Romeo Rizzi ◽  
Luca Nardin
Keyword(s):  
Dynamic Programming ◽  
Polynomial Time ◽  
Broad Class ◽  
Structural Parameters ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
The Other ◽  
Heuristic Optimization ◽  
Negative Fact

The Interactive Knapsacks Heuristic Optimization (IKHO) problem is a particular knapsacks model in which, given an array of knapsacks, every insertion in a knapsack affects also the other knapsacks, in terms of weight and profit. The IKHO model was introduced by Isto Aho to model instances of the load clipping problem. The IKHO problem is known to be APX-hard and, motivated by this negative fact, Aho exhibited a few classes of polynomial instances for the IKHO problem. These instances were obtained by limiting the ranges of two structural parameters, c and u, which describe the extent to which an insertion in a knapsack in uences the nearby knapsacks. We identify a new and broad class of instances allowing for a polynomial time algorithm. More precisely, we show that the restriction of IKHO to instances where is bounded by a constant can be solved in polynomial time, using dynamic programming.

Efficient Processing of XML Tree Pattern Queries

2006 ◽  
Vol 10 (5) ◽  
pp. 738-743 ◽  
Author(s):  
Yangjun Chen ◽  
◽  
Dunren Che ◽  
Keyword(s):  
Dynamic Programming ◽  
Polynomial Time ◽  
Main Idea ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
The Other ◽  
Tree Pattern ◽  
Efficient Processing

In this paper, we present a polynomial-time algorithm for TPQ (tree pattern queries) minimization without XML constraints involved. The main idea of the algorithm is a dynamic programming strategy to find all the matching subtrees within a TPQ. A matching subtree implies a redundancy and should be removed in such a way that the semantics of the original TPQ is not damaged. Our algorithm consists of two parts: one for subtree recognization and the other for subtree deletion. Both of them needs only O(<I>n</I>2) time, where <I>n</I> is the number of nodes in a TPQ.

scholarly journals On the Feasibility and Limitations of Just-in-Time Instruction Set Extension for FPGA-Based Reconfigurable Processors

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Mariusz Grad ◽  
Christian Plessl
Keyword(s):  
Scientific Computing ◽  
Just In Time ◽  
Use Case ◽  
Instruction Set ◽  
Embedded Computing ◽  
Custom Instruction ◽  
Reconfigurable Processors ◽  
Instruction Set Extension ◽  
Instruction Identification ◽  
Instruction Set Processors

Reconfigurable instruction set processors provide the possibility of tailor the instruction set of a CPU to a particular application. While this customization process could be performed during runtime in order to adapt the CPU to the currently executed workload, this use case has been hardly investigated. In this paper, we study the feasibility of moving the customization process to runtime and evaluate the relation of the expected speedups and the associated overheads. To this end, we present a tool flow that is tailored to the requirements of this just-in-time ASIP specialization scenario. We evaluate our methods by targeting our previously introduced Woolcano reconfigurable ASIP architecture for a set of applications from the SPEC2006, SPEC2000, MiBench, and SciMark2 benchmark suites. Our results show that just-in-time ASIP specialization is promising for embedded computing applications, where average speedups of 5x can be achieved by spending 50 minutes for custom instruction identification and hardware generation. These overheads will be compensated if the applications execute for more than 2 hours. For the scientific computing benchmarks, the achievable speedup is only 1.2x, which requires significant execution times in the order of days to amortize the overheads.

Rescheduling Problems with Deteriorating Jobs to Minimize the Tardiness Costs under Time Disruptions

2013 ◽  
Vol 787 ◽  
pp. 973-977
Author(s):  
Xin Gong Zhang
Keyword(s):  
Dynamic Programming ◽  
Polynomial Time ◽  
Single Machine ◽  
Optimal Schedule ◽  
Deteriorating Jobs ◽  
Total Tardiness ◽  
Polynomial Time Algorithms ◽  
Programming Algorithms ◽  
New Jobs

This paper studies the issue of rescheduling to allow for the unexpected arrival of new jobs, taking into account the effect of the disruptions on a previously planned optimal schedule. We consider the single-machine rescheduling problems with deteriorating jobs. Rescheduling means that a set of original jobs has already been scheduled to minimize some classical objective, then a new set of jobs arrives and creates a disruption. The objective is to minimize the total tardiness costs under a limit of the disruptions from the original scheduling. We propose polynomial time algorithms or some dynamic programming algorithms for each problem.

Parallel custom instruction identification for extensible processors

2017 ◽  
Vol 76 ◽  
pp. 149-159 ◽  
Author(s):  
Chenglong Xiao ◽  
Shanshan Wang ◽  
Wanjun Liu ◽  
Emmanuel Casseau
Keyword(s):  
Custom Instruction ◽  
Instruction Identification

Benchmark of Bitrate Adaptation in Video Streaming

2021 ◽  
Vol 13 (4) ◽  
pp. 1-24
Author(s):  
Jessica Chen ◽  
Henry Milner ◽  
Ion Stoica ◽  
Jibin Zhan
Keyword(s):  
Dynamic Programming ◽  
Polynomial Time ◽  
Computation Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Perceptual Quality ◽  
Programming Approach ◽  
Np Hard ◽  
Http Adaptive Streaming ◽  
The Future

The HTTP adaptive streaming technique opened the door to cope with the fluctuating network conditions during the streaming process by dynamically adjusting the volume of the future chunks to be downloaded. The bitrate selection in this adjustment inevitably involves the task of predicting the future throughput of a video session, owing to which various heuristic solutions have been explored. The ultimate goal of the present work is to explore the theoretical upper bounds of the QoE that any ABR algorithm can possibly reach, therefore providing an essential step to benchmarking the performance evaluation of ABR algorithms. In our setting, the QoE is defined in terms of a linear combination of the average perceptual quality and the buffering ratio. The optimization problem is proven to be NP-hard when the perceptual quality is defined by chunk size and conditions are given under which the problem becomes polynomially solvable. Enriched by a global lower bound, a pseudo-polynomial time algorithm along the dynamic programming approach is presented. When the minimum buffering is given higher priority over higher perceptual quality, the problem is shown to be also NP-hard, and the above algorithm is simplified and enhanced by a sequence of lower bounds on the completion time of chunk downloading, which, according to our experiment, brings a 36.0% performance improvement in terms of computation time. To handle large amounts of data more efficiently, a polynomial-time algorithm is also introduced to approximate the optimal values when minimum buffering is prioritized. Besides its performance guarantee, this algorithm is shown to reach 99.938% close to the optimal results, while taking only 0.024% of the computation time compared to the exact algorithm in dynamic programming.

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