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Results 21 - 30
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13,113
Alternative implementations of two-level adaptive branch prediction
- In Proceedings of the 19th International Symposium on Computer Architecture (ISCA-19
, 1992
"... As the issue rate and depth of pipelining of high performance Superscalar processors increase, the importance of an excellent branch predictor becomes more vital to delivering the potential performance of a wide-issue, deep pipelined microarchitecture. We propose a new dynamic branch predictor (Two- ..."
Abstract
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Cited by 327 (22 self)
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their relative effectiveness. We measure the branch prediction accuracy of the three variations of Two-Level Adaptive Branch Prediction, along with several other popular proposed dynamic and static prediction schemes, on the SPEC benchmarks. We show that the average prediction accuracy for TwoLevel Adaptive
PROBEN1 - a set of neural network benchmark problems and benchmarking rules
, 1994
"... Proben1 is a collection of problems for neural network learning in the realm of pattern classification and function approximation plus a set of rules and conventions for carrying out benchmark tests with these or similar problems. Proben1 contains 15 data sets from 12 different domains. All datasets ..."
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Cited by 234 (0 self)
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. This report describes the datasets and the benchmarking rules. It also gives some basic performance measures indicating the difficulty of the various problems. These measures can be used as baselines for comparison.
Leave-One-Out Support Vector Machines
, 1999
"... We present a new learning algorithm for pattern recognition inspired by a recent upper bound on leave--one--out error [ Jaakkola and Haussler, 1999 ] proved for Support Vector Machines (SVMs) [ Vapnik, 1995; 1998 ] . The new approach directly minimizes the expression given by the bound in an attempt ..."
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Cited by 301 (5 self)
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of kernel, it is parameterless -- the selection of the number of training errors is inherent in the algorithm and not chosen by an extra free parameter as in SVMs. First experiments using the method on benchmark datasets from the UCI repository show results similar to SVMs which have been tuned to have
Feature Selection for Classification
- Intelligent Data Analysis
, 1997
"... Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a com ..."
Abstract
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Cited by 299 (9 self)
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of generation procedures and evaluation functions. Representative methods are chosen from each category for detailed explanation and discussion via example. Benchmark datasets with different characteristics are used for comparative study. The strengths and weaknesses of different methods are explained
Caching in the Sprite Network File System
- ACM Transactions on Computer Systems
, 1988
"... The Sprite network operating system uses large main-memory disk block caches to achieve high performance in its file system. It provides non-write-through file caching on both client and server machines. A simple cache consistency mechanism permits files to be shared by multiple clients without dang ..."
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Cited by 296 (12 self)
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danger of stale data. In order to allow the file cache to occupy as much memory as possible, the file system of each machine negotiates with the virtual memory system over physical memory usage and changes the size of the file cache dynamically. Benchmark programs indicate that client caches allow
Escape analysis for Java
- OOPSLA
, 1999
"... This paper presents a simple and efficient data flow algorithm for escape analysis of objects in Java programs to determine (i) if an object can be allocated on the stack; (ii) if an object is accessed only by a single thread duriing its lifetime, so that synchronization operations on that object ca ..."
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Cited by 300 (12 self)
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in the IBM High Per-formance Compiler for Java, are very promising. The percent-age of objects that may be allocated on the stack exceeds 70% of all dynamically created objects in three out of the ten bench-marks (with a median of 19%), 11 % to 92 % of all lock oper-ations are eliminated in those ten
A Framework for Generating Network-Based Moving Objects
- GEOINFORMATICA
, 2002
"... Benchmarking spatiotemporal database systems requires the definition of suitable datasets simulating the typical behavior of moving objects. Previous approaches for generating spatiotemporal data do not consider that moving objects often follow a given network. Therefore, benchmarks require datasets ..."
Abstract
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Cited by 284 (1 self)
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Benchmarking spatiotemporal database systems requires the definition of suitable datasets simulating the typical behavior of moving objects. Previous approaches for generating spatiotemporal data do not consider that moving objects often follow a given network. Therefore, benchmarks require
Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing
, 2011
"... We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algo ..."
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Cited by 239 (27 self)
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We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative
Dynamics of IP traffic: A study of the role of variability and the impact of control
, 1999
"... Using the ns-2-simulator to experiment with different aspects of user- or session-behaviors and network configurations and focusing on the qualitative aspects of a wavelet-based scaling analysis, we present a systematic investigation into how and why variability and feedback-control contribute to th ..."
Abstract
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Cited by 271 (12 self)
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to the intriguing scaling properties observed in actual Internet traces (as our benchmark data, we use measured Internet traffic from an ISP). We illustrate how variability of both user aspects and network environments (i) causes self-similar scaling behavior over large time scales, (ii) determines a more or less
Efficient Path Profiling
- In Proceedings of the 29th Annual International Symposium on Microarchitecture
, 1996
"... A path profile determines how many times each acyclic path in a routine executes. This type of profiling subsumes the more common basic block and edge profiling, which only approximate path frequencies. Path profiles have many potential uses in program performance tuning, profile-directed compilatio ..."
Abstract
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Cited by 287 (8 self)
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benchmarks, path profiling overhead averaged 31%, as compared to 16 % for efficient edge profiling. Path profiling also identifies longer paths than a previous technique, which predicted paths from edge profiles (average of 88, versus 34 instructions). Moreover, profiling shows that the SPEC95 train input
Results 21 - 30
of
13,113
