30,216 research outputs found
Survey on Combinatorial Register Allocation and Instruction Scheduling
Register allocation (mapping variables to processor registers or memory) and
instruction scheduling (reordering instructions to increase instruction-level
parallelism) are essential tasks for generating efficient assembly code in a
compiler. In the last three decades, combinatorial optimization has emerged as
an alternative to traditional, heuristic algorithms for these two tasks.
Combinatorial optimization approaches can deliver optimal solutions according
to a model, can precisely capture trade-offs between conflicting decisions, and
are more flexible at the expense of increased compilation time.
This paper provides an exhaustive literature review and a classification of
combinatorial optimization approaches to register allocation and instruction
scheduling, with a focus on the techniques that are most applied in this
context: integer programming, constraint programming, partitioned Boolean
quadratic programming, and enumeration. Researchers in compilers and
combinatorial optimization can benefit from identifying developments, trends,
and challenges in the area; compiler practitioners may discern opportunities
and grasp the potential benefit of applying combinatorial optimization
Flexible resources allocation techniques: characteristics and modelling
At the interface between engineering, economics, social sciences and humanities, industrial engineering aims to provide answers to various sectors of business problems. One of these problems is the adjustment between the workload needed by the work to be realised and the availability of the company resources. The objective of this work is to help to find a methodology for the allocation of flexible human resources in industrial activities planning and scheduling. This model takes into account two levers of flexibility, one related to the working time modulation, and the other to the varieties of tasks that can be performed by a given resource (multi–skilled actor). On the one hand, multi–skilled actors will help to guide the various choices of the allocation to appreciate the impact of these choices on the tasks durations. On the other hand, the working time modulation that allows actors to have a work planning varying according to the workload which the company has to face
When Queueing Meets Coding: Optimal-Latency Data Retrieving Scheme in Storage Clouds
In this paper, we study the problem of reducing the delay of downloading data
from cloud storage systems by leveraging multiple parallel threads, assuming
that the data has been encoded and stored in the clouds using fixed rate
forward error correction (FEC) codes with parameters (n, k). That is, each file
is divided into k equal-sized chunks, which are then expanded into n chunks
such that any k chunks out of the n are sufficient to successfully restore the
original file. The model can be depicted as a multiple-server queue with
arrivals of data retrieving requests and a server corresponding to a thread.
However, this is not a typical queueing model because a server can terminate
its operation, depending on when other servers complete their service (due to
the redundancy that is spread across the threads). Hence, to the best of our
knowledge, the analysis of this queueing model remains quite uncharted.
Recent traces from Amazon S3 show that the time to retrieve a fixed size
chunk is random and can be approximated as a constant delay plus an i.i.d.
exponentially distributed random variable. For the tractability of the
theoretical analysis, we assume that the chunk downloading time is i.i.d.
exponentially distributed. Under this assumption, we show that any
work-conserving scheme is delay-optimal among all on-line scheduling schemes
when k = 1. When k > 1, we find that a simple greedy scheme, which allocates
all available threads to the head of line request, is delay optimal among all
on-line scheduling schemes. We also provide some numerical results that point
to the limitations of the exponential assumption, and suggest further research
directions.Comment: Original accepted by IEEE Infocom 2014, 9 pages. Some statements in
the Infocom paper are correcte
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