Computation demands on mobile and edge devices are increasing dramatically. Mobile devices, such as smart phones, incorporate a large number of dedicated accelerators and fixed-function hardware blocks to deliver the required performance and power efficiency. Due to the heterogeneous nature of these devices, they feature vastly larger design spaces than traditional systems featuring only a CPU. Currently, academia struggles to fully evaluate such heterogeneous systems on chip due to the limited access and availability of proprietary workloads. To address these challenges, we propose Mocktails: a methodology to synthetically recreate the varying spatio-temporal memory access behaviour of proprietary heterogeneous compute devices. We focus on capturing the interspersed address streams of the workload and the burstiness of the injection process for proprietary compute devices commonly found in mobile systems. We evaluate Mocktails in simulation with proprietary memory traces of IP blocks. Mocktails accurately recreates the dynamic behaviour of memory access scheduling for memory controller metrics including read row hits (at most 7.3% error) and write row hits (at most 2.8% error). Architects can use Mocktails in their simulations as a substitute for a proprietary compute device, making the tool a useful conduit between industry and academia.

Trace-driven simulation of chip multiprocessor (CMP) systems offers many advantages over execution-driven simulation, such as reducing simulation time and complexity, allowing portability, and scalability. However, trace-based simulation approaches have difficulty capturing and accurately replaying multithreaded traces due to the inherent nondeterminism in the execution of multithreaded programs. In this work, we present SynchroTrace, a scalable, flexible, and accurate trace-based multithreaded simulation methodology. By recording synchronization events relevant to modern threading libraries (e.g., Pthreads and OpenMP) and dependencies in the traces, independent of the host architecture, the methodology is able to accurately model the nondeterminism of multithreaded programs for different hardware platforms and threading paradigms. Through capturing high-level instruction categories, the SynchroTrace average CPI trace Replay timing model offers fast and accurate simulation of many-core in-order CMPs. We perform two case studies to validate the SynchroTrace simulation flow against the gem5 full-system simulator: (1) a constraint-based design space exploration with traditional CMP benchmarks and (2) a thread-scalability study with HPC-representative applications. The results from these case studies show that (1) our trace-based approach with trace filtering has a peak speedup of up to 18.7× over simulation in gem5 full-system with an average of 9.6× speedup, (2) SynchroTrace maintains the thread-scaling accuracy of gem5 and can efficiently scale up to 64 threads, and (3) SynchroTrace can trace in one platform and model any platform in early stages of design.

Exascale computing will get mankind closer to solving important social, scientific and engineering problems. Due to high prototyping costs, High Performance Computing (HPC) system architects make use of simulation models for design space exploration and hardware-software co-design. However, as HPC systems reach exascale proportions, the cost of simulation increases, since simulators themselves are largely single-threaded. Tools for selecting representative parts of parallel applications to reduce running costs are widespread, e.g., BarrierPoint achieves this by analysing, in simulation, abstract characteristics such as basic blocks and reuse distances. However, architectures new to HPC have a limited set of tools available. In this work, we provide an independent cross-architectural evaluation on real hardware — across Intel and ARM — of the BarrierPoint methodology, when applied to parallel HPC proxy applications. We present both cases: when the methodology can be applied and when it cannot. In the former case, results show that we can predict the performance of full application execution by running shorter representative sections. In the latter case, we dive into the underlying issues and suggest improvements. We demonstrate a total simulation time reduction of up to 178x, whilst keeping the error below 2.3% for both cycles and instructions.

As computer systems become increasingly complex, the need for fast and accurate simulation tools increases. Accurate but slow processor core models are often substituted with simple trace players to achieve faster memory-system simulation. However, existing trace-driven simulation techniques are limited in their applicability and availability. In this work, we capture elastic traces containing out-of-order core dependencies and effects of speculative execution, which overcome limitations of existing work. Additionally, we make our capture and replay modelling available in the gem5 simulator. Our trace-driven CPU achieves a speed-up of 6-8x compared to the reference core and predicts the performance with less than 1% error on average when the memory-system is changed.