Processor scheduling in shared memory multiprocessors
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Eraser: a dynamic data race detector for multithreaded programs
ACM Transactions on Computer Systems (TOCS)
Thread scheduling for multiprogrammed multiprocessors
Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures
Type-based race detection for Java
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Automatic Detection of Parallelism: A Grand Challenge for High-Performance Computing
IEEE Parallel & Distributed Technology: Systems & Technology
Cooperative Task Management Without Manual Stack Management
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
CIL: Intermediate Language and Tools for Analysis and Transformation of C Programs
CC '02 Proceedings of the 11th International Conference on Compiler Construction
A type and effect system for atomicity
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Efficient on-the-fly data race detection in multithreaded C++ programs
Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming
RacerX: effective, static detection of race conditions and deadlocks
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Run-Time Support for the Automatic Parallelization of Java Programs
The Journal of Supercomputing
RaceTrack: efficient detection of data race conditions via adaptive tracking
Proceedings of the twentieth ACM symposium on Operating systems principles
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Many applications take advantage of parallelism to increase performance. Servers are a particularly common case as they must multiplex resources across many simultaneous users. Unfortunately, writing concurrent applications is difficult and prone to subtle and non-deterministic bugs that are difficult to reproduce. We advocate an approach to developing concurrent programs that is safe by default. Conservative static analysis determines when two code segments may safely run in parallel, and a runtime scheduler respects these constraints. We have built an analyzer for event-driven servers that discovers data sharing to find safe parallelism among event handlers. As a prototype, the analysis currently considers only global data, assuming that request-specific data structures passed to event handlers are completely independent. We have also begun work on a runtime system that schedules event handler execution within the constraints determined by the analyzer. For performance reasons, the scheduler makes additional conservative assumptions about contention. We have analyzed thttpd, an event-driven web server. We show how our system can be used to increase performance without complex synchronization schemes.