344,462 research outputs found
Ranking Functions for Size-Change Termination II
Size-Change Termination is an increasingly-popular technique for verifying
program termination. These termination proofs are deduced from an abstract
representation of the program in the form of "size-change graphs".
We present algorithms that, for certain classes of size-change graphs, deduce
a global ranking function: an expression that ranks program states, and
decreases on every transition. A ranking function serves as a witness for a
termination proof, and is therefore interesting for program certification. The
particular form of the ranking expressions that represent SCT termination
proofs sheds light on the scope of the proof method. The complexity of the
expressions is also interesting, both practicaly and theoretically.
While deducing ranking functions from size-change graphs has already been
shown possible, the constructions in this paper are simpler and more
transparent than previously known. They improve the upper bound on the size of
the ranking expression from triply exponential down to singly exponential (for
certain classes of instances). We claim that this result is, in some sense,
optimal. To this end, we introduce a framework for lower bounds on the
complexity of ranking expressions and prove exponential lower bounds.Comment: 29 pages
On the Complexity of List Ranking in the Parallel External Memory Model
We study the problem of list ranking in the parallel external memory (PEM)
model. We observe an interesting dual nature for the hardness of the problem
due to limited information exchange among the processors about the structure of
the list, on the one hand, and its close relationship to the problem of
permuting data, which is known to be hard for the external memory models, on
the other hand.
By carefully defining the power of the computational model, we prove a
permuting lower bound in the PEM model. Furthermore, we present a stronger
\Omega(log^2 N) lower bound for a special variant of the problem and for a
specific range of the model parameters, which takes us a step closer toward
proving a non-trivial lower bound for the list ranking problem in the
bulk-synchronous parallel (BSP) and MapReduce models. Finally, we also present
an algorithm that is tight for a larger range of parameters of the model than
in prior work
Measuring economic complexity of countries and products: which metric to use?
Evaluating the economies of countries and their relations with products in
the global market is a central problem in economics, with far-reaching
implications to our theoretical understanding of the international trade as
well as to practical applications, such as policy making and financial
investment planning. The recent Economic Complexity approach aims to quantify
the competitiveness of countries and the quality of the exported products based
on the empirical observation that the most competitive countries have
diversified exports, whereas developing countries only export few low quality
products -- typically those exported by many other countries. Two different
metrics, Fitness-Complexity and the Method of Reflections, have been proposed
to measure country and product score in the Economic Complexity framework. We
use international trade data and a recent ranking evaluation measure to
quantitatively compare the ability of the two metrics to rank countries and
products according to their importance in the network. The results show that
the Fitness-Complexity metric outperforms the Method of Reflections in both the
ranking of products and the ranking of countries. We also investigate a
Generalization of the Fitness-Complexity metric and show that it can produce
improved rankings provided that the input data are reliable
Scalable Probabilistic Similarity Ranking in Uncertain Databases (Technical Report)
This paper introduces a scalable approach for probabilistic top-k similarity
ranking on uncertain vector data. Each uncertain object is represented by a set
of vector instances that are assumed to be mutually-exclusive. The objective is
to rank the uncertain data according to their distance to a reference object.
We propose a framework that incrementally computes for each object instance and
ranking position, the probability of the object falling at that ranking
position. The resulting rank probability distribution can serve as input for
several state-of-the-art probabilistic ranking models. Existing approaches
compute this probability distribution by applying a dynamic programming
approach of quadratic complexity. In this paper we theoretically as well as
experimentally show that our framework reduces this to a linear-time complexity
while having the same memory requirements, facilitated by incremental accessing
of the uncertain vector instances in increasing order of their distance to the
reference object. Furthermore, we show how the output of our method can be used
to apply probabilistic top-k ranking for the objects, according to different
state-of-the-art definitions. We conduct an experimental evaluation on
synthetic and real data, which demonstrates the efficiency of our approach
The Blacklisting Memory Scheduler: Balancing Performance, Fairness and Complexity
In a multicore system, applications running on different cores interfere at
main memory. This inter-application interference degrades overall system
performance and unfairly slows down applications. Prior works have developed
application-aware memory schedulers to tackle this problem. State-of-the-art
application-aware memory schedulers prioritize requests of applications that
are vulnerable to interference, by ranking individual applications based on
their memory access characteristics and enforcing a total rank order.
In this paper, we observe that state-of-the-art application-aware memory
schedulers have two major shortcomings. First, such schedulers trade off
hardware complexity in order to achieve high performance or fairness, since
ranking applications with a total order leads to high hardware complexity.
Second, ranking can unfairly slow down applications that are at the bottom of
the ranking stack. To overcome these shortcomings, we propose the Blacklisting
Memory Scheduler (BLISS), which achieves high system performance and fairness
while incurring low hardware complexity, based on two observations. First, we
find that, to mitigate interference, it is sufficient to separate applications
into only two groups. Second, we show that this grouping can be efficiently
performed by simply counting the number of consecutive requests served from
each application.
We evaluate BLISS across a wide variety of workloads/system configurations
and compare its performance and hardware complexity, with five state-of-the-art
memory schedulers. Our evaluations show that BLISS achieves 5% better system
performance and 25% better fairness than the best-performing previous scheduler
while greatly reducing critical path latency and hardware area cost of the
memory scheduler (by 79% and 43%, respectively), thereby achieving a good
trade-off between performance, fairness and hardware complexity
Hierarchical meta-rules for scalable meta-learning
The Pairwise Meta-Rules (PMR) method proposed in [18] has been shown to improve the predictive performances of several metalearning algorithms for the algorithm ranking problem. Given m target objects (e.g., algorithms), the training complexity of the PMR method with respect to m is quadratic: (formula presented). This is usually not a problem when m is moderate, such as when ranking 20 different learning algorithms. However, for problems with a much larger m, such as the meta-learning-based parameter ranking problem, where m can be 100+, the PMR method is less efficient. In this paper, we propose a novel method named Hierarchical Meta-Rules (HMR), which is based on the theory of orthogonal contrasts. The proposed HMR method has a linear training complexity with respect to m, providing a way of dealing with a large number of objects that the PMR method cannot handle efficiently. Our experimental results demonstrate the benefit of the new method in the context of meta-learning
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