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Generic multi-threaded sampled simulation has been a long-standing, challenging problem with the potential to help change how researchers study modern, complex computing systems. Yet, a practical solution for reducing complex multi-threaded applications into a representative sample has been elusive. Existing techniques either do not provide significant speedups to be useful (Time-Based Sampling techniques can show less than a 10× speedup compared to a fully-detailed simulation) or apply only to particular synchronization types (BarrierPoint for barrier-based workloads or SMARTS for server workloads). In addition, workload-specific solutions, like BarrierPoint, are rigid with respect to region selection, which can limit the overall simulation speedup when intra-barrier regions are large. A solution is needed that both supports generic multi-threaded applications, no matter the synchronization primitives used, as well as allows for ease of deployment and fast evaluation. In this work, we aim to solve these challenges and propose a novel sampling technique for multi-threaded applications, called LoopPoint, that is both agnostic to the type of synchronization primitives used and scales by the similarity exhibited by the application. The proposed methodology combines several vital features, including (1) repeatable, up-front application analysis, (2) a novel clustering approach to take into account run-time parallelism, and (3) the use of loop-based simulation markers to divide the work into measurable chunks, even in the presence of spin-loops. LoopPoint identifies representative simulation regions that can be simulated in parallel to achieve speedups of up to 801x for the train input set of the multi-threaded SPEC CPU2017 benchmarks with an average simulation error of just 2.33%. For the ref inputs of CPU2017, we calculate the speedup with LoopPoint to be 11,587x on average (for parallel simulation), and up to 31,253x, demonstrating how the identification of application regularity and loops can lead to significant simulation improvements compared to state-of-the-art solutions.

Prestressed concrete pipe has been mostly used in water-transmission line. In this paper we present the full-scale of our studies of PCCP and the evaluation of delimitation behavior of the structure. Recent attempt has been made to enable the PCCP structure suitable for the conventional cut and cover method. In this way, the disturbance to structures, construction time, and the cost can be decreased., {"references":["Alavinasab, A., Jha, R., & Ahmadi, G. (2011). Damage identification based on modal analysis of prestressed concrete pipes. In Pipelines 2011: A Sound Conduit for Sharing Solutions (pp. 12-23).","AWWA C301-07 (2007). Prestressed Concrete Cylinder Pipe, Steel Cylinder Pipe. American Water Works Association, Denver, CO.","Kola, R., & Sinha, S. K. (2010). Predictability and Preventability Indices for PCCP Water Mains. In Pipelines 2010: Climbing New Peaks to Infrastructure Reliability: Renew, Rehab, and Reinvest (pp. 730- 742).","Piratla, K. R., & Ariaratnam, S. T. (2011). Criticality analysis of water distribution pipelines. Journal of Pipeline Systems Engineering and Practice, 2(3), 91-101."]}

Modern architecture research relies heavily on detailed pipeline simulation. A time consuming part of building a simulator is correctly emulating the operating system effects when performing application-level simulation. This requires hand coding the emulation of any system effect (e.g., system call, interrupts, DMA transfers) used by the workload. This can be tedious, especially when having to support running the simulator on completely different operating systems or different versions of the operating system. Once a simulator has correctly emulated the system effects, it can also be costly to maintain this emulation, since the emulation can break with new versions of the operating system the simulator is running on. In addition, system effect emulation can cause some variance in simulation results. In this paper we describe an approach that automatically logs operating system effects to guide architecture simulation of user code. The benefit of this approach is that (a) we do not have to build or support any infrastructure for emulating operating system effects, and (b) the system effect logs provide deterministic simulation in the presence of these system effects.Pre-2018 CSE ID: CS2005-0840