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System emulation is widely used in today's computer systems. This technology opens new opportunities for resource sharing as well as enhancing system security and reliability. System emulation across different instruction set architectures (ISA) can enable further opportunities. For example, cross-ISA emulation can enable workload consolidation over a wide range of microprocessors and potentially facilitate the seamless deployment of new processor architectures. As multicore and manycore processors become pervasive, it is important to address the challenges toward supporting system emulation on these platforms. A key challenge in cross-ISA emulation on multicore systems is ensuring the correctness of emulation when the guest and the host memory consistency models differ. Many existing cross-ISA system emulators are sequential, thus they are able to avoid this problem at the cost of significant performance degradation. Recently proposed parallel emulators are able to address the performance limitation; however, they provide limited support for memory consistency model emulation. When the host system has a weaker memory consistency model compared to the guest system, the emulator can insert memory fences at appropriate locations in the translated code to enforce the guest memory ordering constraints. These memory fences can significantly degrade the performance of the translated code. Transactional execution support available on certain recent microprocessors provides an alternative approach. Transactional execution of the translated code enforces sequential consistency (SC) at the coarsegrained transaction level, which in turn ensures that all memory accesses made on the host machine conform to SC. Enforcing SC on the host machine guarantees that the emulated execution will be correct for any guest memory model. In this article, we compare and evaluate the overheads associated with using transactions and fences for memory consistency model emulation on the Intel Haswell processor. Our experience of implementing these two approaches on a state-of-the-art parallel emulator, COREMU, demonstrates that memory consistency model emulation using transactions performs better when the transaction sizes are large enough to amortize the transaction overhead and the transaction conflict rate is low, whereas inserting memory fences is better for applications in which the transaction overhead is high. A hybrid implementation that dynamically determines which approach to invoke can outperform both approaches. Our results, based on the SPLASH-2 and the PARSEC benchmark suites, demonstrate that the proposed hybrid approach is able to outperform the fence insertion mechanism by 4.9% and the transactional execution approach by 24.9% for two-thread applications, and outperform them by 4.5% and 44.7%, respectively, for four-threaded execution.

For today's increasingly power-constrained multicore systems, integrating simpler and more energy-efficient in-order cores becomes attractive. However, since in-order processors lack complex hardware support for tolerating long-latency memory accesses, developing compiler technologies to hide such latencies becomes critical. Compiler-directed prefetching has been demonstrated effective on some applications. On the application side, a large class of data centric applications has emerged to explore the underlying properties of the explosively growing data. These applications, in contrast to traditional benchmarks, are characterized by substantial thread-level parallelism, complex and unpredictable control flow, as well as intensive and irregular memory access patterns. These applications are expected to be the dominating workloads on future microprocessors. Thus, in this paper, we investigated the effectiveness of compiler-directed prefetching on data mining applications in in-order multicore systems. Our study reveals that although properly inserted prefetch instructions can often effectively reduce memory access latencies for data mining applications, the compiler is not always able to exploit this potential. Compiler-directed prefetching can become inefficient in the presence of complex control flow and memory access patterns; and architecture dependent behaviors. The integration of multithreaded execution onto a single die makes it even more difficult for the compiler to insert prefetch instructions, since optimizations that are effective for single-threaded execution may or may not be effective in multithreaded execution. Thus, compiler-directed prefetching must be judiciously deployed to avoid creating performance bottlenecks that otherwise do not exist. Our experiences suggest that dynamic performance tuning techniques that adjust to the behaviors of a program can potentially facilitate the deployment of aggressive optimizations in data mining applications.