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Jerry James

dblp:54/5885 · DBLP profile ↗
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6ranked-venue papers
6as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 4 · 4 first-authorTheory of computation · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
3 papers
Distributed computing theory · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed computing theory
shared memory consistency
0.021997
Fault Tolerance Bounds for Memory Consistency · PODC 1997
Complete Implementations for Shared Memory Consistency Conditions (Abstract) · PODC 1995
Distributed systems
fault tolerance
0.011999
External Logging on a Logical Ring · PODC 1999
YearPublicationVenuePosition
2001 Recovering distributed objects
Jerry James, Ambuj K. Singh
Inf. Process. Lett.1
2000 Design of the Kan distributed object system
abstract
Distributed software problems are often addressed with object-oriented solutions. Objects provide the benefits of encapsulation and abstraction that have proven useful in managing the complexity of sequential code. However, the management of distributed objects is typically by means of complex APIs, such as CORBA, DCOM, or Java RMI. The complexity of the APIs is itself a hurdle to the writing of efficient, robust programs. An alternate approach is to provide the programmer with a simple interface to an underlying object management layer that provides efficient access to objects and sufficient power for common distributed programming tasks. This paper describes the implementation of the Kan system. It has a clear, simple object model with powerful semantics, embodying such concepts as atomic transactions, asynchronous method calls, and multithreading. The model primitives help the programmer avoid common concurrent programming errors, allowing clean expressions of concurrent algorithms. Kan provides distributed objects (i.e. objects that can migrate or be replicated), rather than the remote objects of Java RMI. Nevertheless, Kan optimizations provide runtime object accesses that are as efficient as or more efficient than accesses made to a similar distribution control layer over Java RMI. We describe the optimizations and measure their runtime impacts. Copyright © 2000 John Wiley & Sons, Ltd.
Jerry James, Ambuj K. Singh
Concurr. Pract. Exp.1
1999 External Logging on a Logical Ring
abstract
No abstract available.
Jerry James, Ambuj K. Singh
PODC1
1997 Fault Tolerance Bounds for Memory Consistency
abstract
No abstract available.
Jerry James, Ambuj K. Singh
PODC1
1996 The Impact of Hardware Models on Shared Memory Consistency Conditions
Jerry James, Ambuj K. Singh
CONCUR1
1995 Complete Implementations for Shared Memory Consistency Conditions (Abstract)
abstract
No abstract available.
Jerry James, Ambuj K. Singh
PODC1