Despite a large research effort, software distributed shared memory systems have not been widely used to run parallel applications across clusters of computers. The higher performance of hardware multiprocessors makes them the preferred platform for developing and executing applications. In addition, most applications are distributed only in binary format for a handful of popular hardware systems. Due to their limited functionality, software systems cannot directly execute the applications developed for hardware platforms. We have developed a system called Shasta that attempts to address the issues of efficiency and transparency that have hindered wider acceptance of software systems. Shasta is a distributed shared memory system that supports coherence at a fine granularity in software and can efficiently exploit small-scale SMP nodes by allowing processes on the same node to share data at hardware speeds. Thispaper focuses onourgoal oftappingintolarge classes of commercially available applications by transparently executing the same binaries that run on hardware platforms. We discuss the issues involved in achieving transparent execution of binaries, which include supportingthe full instruction set architecture, implementing an appropriate memory consistency model, and extending OS services across separate nodes. We also describe the techniques used in Shasta to solve the above problems. The Shasta system is fully functional on a prototype cluster of Alpha multiprocessors connected through Digital’s Memory Channel network and can transparentlyrunparallelapplicationsontheclusterthatwere compiled to run on a single shared-memory multiprocessor. As an example of Shasta’s flexibility, it can execute Oracle 7.3, a commercial database engine, across the cluster, includingworkloadsmodeled after theTPC-B and TPC-D database benchmarks. To characterize the performance of the system and the cost of providing complete transparency, we present performance results for microbenchmarks and applications runningon the cluster, include preliminary results for Oracle runs.
{"title":"Towards transparent and efficient software distributed shared memory","authors":"D. Scales, K. Gharachorloo","doi":"10.1145/268998.266673","DOIUrl":"https://doi.org/10.1145/268998.266673","url":null,"abstract":"Despite a large research effort, software distributed shared memory systems have not been widely used to run parallel applications across clusters of computers. The higher performance of hardware multiprocessors makes them the preferred platform for developing and executing applications. In addition, most applications are distributed only in binary format for a handful of popular hardware systems. Due to their limited functionality, software systems cannot directly execute the applications developed for hardware platforms. We have developed a system called Shasta that attempts to address the issues of efficiency and transparency that have hindered wider acceptance of software systems. Shasta is a distributed shared memory system that supports coherence at a fine granularity in software and can efficiently exploit small-scale SMP nodes by allowing processes on the same node to share data at hardware speeds. Thispaper focuses onourgoal oftappingintolarge classes of commercially available applications by transparently executing the same binaries that run on hardware platforms. We discuss the issues involved in achieving transparent execution of binaries, which include supportingthe full instruction set architecture, implementing an appropriate memory consistency model, and extending OS services across separate nodes. We also describe the techniques used in Shasta to solve the above problems. The Shasta system is fully functional on a prototype cluster of Alpha multiprocessors connected through Digital’s Memory Channel network and can transparentlyrunparallelapplicationsontheclusterthatwere compiled to run on a single shared-memory multiprocessor. As an example of Shasta’s flexibility, it can execute Oracle 7.3, a commercial database engine, across the cluster, includingworkloadsmodeled after theTPC-B and TPC-D database benchmarks. To characterize the performance of the system and the cost of providing complete transparency, we present performance results for microbenchmarks and applications runningon the cluster, include preliminary results for Oracle runs.","PeriodicalId":340271,"journal":{"name":"Proceedings of the sixteenth ACM symposium on Operating systems principles","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130366759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new model for controlling information flo w in systems with mutual distrust and decentralized authority. The model allows users to share information with distrusted code (e.g., downloaded applets), yet still control how that code disseminates the shared information to others. The model improves on existing multilevel security models by allowing users to declassify information in a decentralized way, and by improving support for fine-graineddata sharing. The paper also shows how static program analysis can be used to certify proper information flo ws in this model and to avoid most run-time information flo w checks.
{"title":"A decentralized model for information flow control","authors":"A. Myers, B. Liskov","doi":"10.1145/268998.266669","DOIUrl":"https://doi.org/10.1145/268998.266669","url":null,"abstract":"This paper presents a new model for controlling information flo w in systems with mutual distrust and decentralized authority. The model allows users to share information with distrusted code (e.g., downloaded applets), yet still control how that code disseminates the shared information to others. The model improves on existing multilevel security models by allowing users to declassify information in a decentralized way, and by improving support for fine-graineddata sharing. The paper also shows how static program analysis can be used to certify proper information flo ws in this model and to avoid most run-time information flo w checks.","PeriodicalId":340271,"journal":{"name":"Proceedings of the sixteenth ACM symposium on Operating systems principles","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132029197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jennifer M. Anderson, Lance M. Berc, J. Dean, S. Ghemawat, Monika Henzinger, Shun-Tak Leung, R. L. Sites, Mark T. Vandevoorde, Carl A. Waldspurger, W. Weihl
This paper describes the DIGlTAL Continuous Profiling Infrastmcture, a sampling-based profiling system designed to run continuously on production systems. The system supports multiprocessors, works on unmodified executable& and collects profiles for entire systems, including user programs, shared libraries, and the operating system kernel. Samples are collected at a high rate (over 5200 samples/secper333-MHz processor), yet with low overhead (l-3% slowdown for most workloads). Analysis tools supplied with the profiling system use the sample data to produce an accurate accounting, down to the level of pipeline stalls incurred by individual instructions, of where time is being spent. When instructions incur stalls, the tools identify possible reasons, such as cache misses, branch mispredictions, and functional unit contention. The fine-grained instruction-level analysis guides users and automated optimizers to the causes of performance problems and provides important insights for fixing them.
{"title":"Continuous profiling: where have all the cycles gone?","authors":"Jennifer M. Anderson, Lance M. Berc, J. Dean, S. Ghemawat, Monika Henzinger, Shun-Tak Leung, R. L. Sites, Mark T. Vandevoorde, Carl A. Waldspurger, W. Weihl","doi":"10.1145/268998.266637","DOIUrl":"https://doi.org/10.1145/268998.266637","url":null,"abstract":"This paper describes the DIGlTAL Continuous Profiling Infrastmcture, a sampling-based profiling system designed to run continuously on production systems. The system supports multiprocessors, works on unmodified executable& and collects profiles for entire systems, including user programs, shared libraries, and the operating system kernel. Samples are collected at a high rate (over 5200 samples/secper333-MHz processor), yet with low overhead (l-3% slowdown for most workloads). Analysis tools supplied with the profiling system use the sample data to produce an accurate accounting, down to the level of pipeline stalls incurred by individual instructions, of where time is being spent. When instructions incur stalls, the tools identify possible reasons, such as cache misses, branch mispredictions, and functional unit contention. The fine-grained instruction-level analysis guides users and automated optimizers to the causes of performance problems and provides important insights for fixing them.","PeriodicalId":340271,"journal":{"name":"Proceedings of the sixteenth ACM symposium on Operating systems principles","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128648807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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