Performance Evaluation of the Orca Shared Object System

• Main Authors: Henri E. Bal, Raoul Bhoedjang, Rutger Hofman, Ceriel Jacobs, Koen Langendoen, Tim Rühl, M. Frans Kaashoek
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 1998
• Subjects: Classifications—Concurrent; distributed; and parallel languages; D.3.4 [Programming Languages; Processors—Compilers; Run-time environments General Terms; Languages; Design; Performance Additional Key Words and Phrases; distributed shared memory; parallel processing; portability; Orca
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.3581; http://www.cs.cornell.edu/raoul/papers/tocs98.ps

Orca is a portable, object-based distributed shared memory system. This paper studies and evaluates the design choices made in the Orca system and compares Orca with other DSMs. The paper gives a quantitative analysis of Orca’s coherence protocol (based on write-updates with function shipping), the totally-ordered group communication protocol, the strategy for object placement, and the all-software, user-space architecture. Performance measurements for ten parallel applications illustrate the tradeoffs made in the design of Orca, and also show that essentially the right design decisions have been made. A write-update protocol with function shipping is effective for Orca, especially since it is used in combination with techniques that avoid replicating objects that have a low read/write ratio. The overhead of totally-ordered group communication on application performance is low. The Orca system is able to make near-optimal decisions for object placement and replication. In addition, the paper compares the performance of Orca with that of a page-based DSM (TreadMarks) and another object-based DSM (CRL). It also analyses the communication overhead of the DSMs for several applications. All performance measurements are done on a 32-node Pentium Pro cluster with Myrinet and Fast Ethernet networks. The results show that the Orca programs send fewer messages and less data than the TreadMarks and CRL programs and also obtain better speedups.