Gilles A. Pokam, Klaus Danne, C. Pereira, R. Kassa, T. Kranich, Shiliang Hu, Justin Emile Gottschlich, N. Honarmand, Nathan Dautenhahn, Samuel T. King, J. Torrellas
{"title":"QuickRec:一个用于记录和回放多线程程序的英特尔架构扩展原型","authors":"Gilles A. Pokam, Klaus Danne, C. Pereira, R. Kassa, T. Kranich, Shiliang Hu, Justin Emile Gottschlich, N. Honarmand, Nathan Dautenhahn, Samuel T. King, J. Torrellas","doi":"10.1145/2485922.2485977","DOIUrl":null,"url":null,"abstract":"There has been significant interest in hardware-assisted deterministic Record and Replay (RnR) systems for multithreaded programs on multiprocessors. However, no proposal has implemented this technique in a hardware prototype with full operating system support. Such an implementation is needed to assess RnR practicality. This paper presents QuickRec, the first multicore Intel Architecture (IA) prototype of RnR for multithreaded programs. QuickRec is based on QuickIA, an Intel emulation platform for rapid prototyping of new IA extensions. QuickRec is composed of a Xeon server platform with FPGA-emulated second-generation Pentium cores, and Capo3, a full software stack for managing the recording hardware from within a modified Linux kernel. This paper's focus is understanding and evaluating the implementation issues of RnR on a real platform. Our effort leads to some lessons learned, as well as to some pointers for future research. We demonstrate that RnR can be implemented efficiently on a real multicore IA system. In particular, we show that the rate of memory log generation is insignificant, and that the recording hardware has negligible performance overhead. However, the software stack incurs an average recording overhead of nearly 13%, which must be reduced to enable always-on use of RnR.","PeriodicalId":20555,"journal":{"name":"Proceedings of the 40th Annual International Symposium on Computer Architecture","volume":"5 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"QuickRec: prototyping an intel architecture extension for record and replay of multithreaded programs\",\"authors\":\"Gilles A. Pokam, Klaus Danne, C. Pereira, R. Kassa, T. Kranich, Shiliang Hu, Justin Emile Gottschlich, N. Honarmand, Nathan Dautenhahn, Samuel T. King, J. Torrellas\",\"doi\":\"10.1145/2485922.2485977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There has been significant interest in hardware-assisted deterministic Record and Replay (RnR) systems for multithreaded programs on multiprocessors. However, no proposal has implemented this technique in a hardware prototype with full operating system support. Such an implementation is needed to assess RnR practicality. This paper presents QuickRec, the first multicore Intel Architecture (IA) prototype of RnR for multithreaded programs. QuickRec is based on QuickIA, an Intel emulation platform for rapid prototyping of new IA extensions. QuickRec is composed of a Xeon server platform with FPGA-emulated second-generation Pentium cores, and Capo3, a full software stack for managing the recording hardware from within a modified Linux kernel. This paper's focus is understanding and evaluating the implementation issues of RnR on a real platform. Our effort leads to some lessons learned, as well as to some pointers for future research. We demonstrate that RnR can be implemented efficiently on a real multicore IA system. In particular, we show that the rate of memory log generation is insignificant, and that the recording hardware has negligible performance overhead. However, the software stack incurs an average recording overhead of nearly 13%, which must be reduced to enable always-on use of RnR.\",\"PeriodicalId\":20555,\"journal\":{\"name\":\"Proceedings of the 40th Annual International Symposium on Computer Architecture\",\"volume\":\"5 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 40th Annual International Symposium on Computer Architecture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2485922.2485977\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 40th Annual International Symposium on Computer Architecture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2485922.2485977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
QuickRec: prototyping an intel architecture extension for record and replay of multithreaded programs
There has been significant interest in hardware-assisted deterministic Record and Replay (RnR) systems for multithreaded programs on multiprocessors. However, no proposal has implemented this technique in a hardware prototype with full operating system support. Such an implementation is needed to assess RnR practicality. This paper presents QuickRec, the first multicore Intel Architecture (IA) prototype of RnR for multithreaded programs. QuickRec is based on QuickIA, an Intel emulation platform for rapid prototyping of new IA extensions. QuickRec is composed of a Xeon server platform with FPGA-emulated second-generation Pentium cores, and Capo3, a full software stack for managing the recording hardware from within a modified Linux kernel. This paper's focus is understanding and evaluating the implementation issues of RnR on a real platform. Our effort leads to some lessons learned, as well as to some pointers for future research. We demonstrate that RnR can be implemented efficiently on a real multicore IA system. In particular, we show that the rate of memory log generation is insignificant, and that the recording hardware has negligible performance overhead. However, the software stack incurs an average recording overhead of nearly 13%, which must be reduced to enable always-on use of RnR.