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There is a growing concern about the increasing rate of defects in computing substrates. Traditional redundancy solutions prove to be too expensive for commodity microprocessor systems. Modern microprocessors feature multiple execution units to take advantage of instruction level parallelism. However, most workloads do not exhibit the level of instruction level parallelism that a typical microprocessor is resourced for. This offers an opportunity to reexecute instructions using idle execution units. But, relying solely on idle resources will not provide full instruction coverage and there is a need to explore other alternatives. To that end, we propose and evaluate two instruction replay schemes within the same core for online testing of the execution units. One scheme (RER) reexecutes only the retired instructions, while the other (REI) reexecutes all the issued instructions. The complete proposed solution requires a comparator and minor modifications to control logic, resulting in negligible hardware overhead. Both soft and hard error detection are considered and the performance and energy impact of both schemes are evaluated and compared against previously proposed redundant execution schemes. Results show that even though the proposed schemes result in a small performance penalty when compared to previous work, the energy overhead is significantly reduced.

With the advent of multicore processors the emphasis incomputation is moving from sequential to parallel processing. Still, applications that require strong sequential performance do not achieve their highest performance/power when executing on current multicoresystems. As the computational needs vary significantly across different applications and with time, there is a need to dynamically allocate appropriate computational resources on demand to suit the applications' current needs, in order to minimize the energy consumption. The Energy per Instruction (EPI) could be further decreased by dynamically adapting the voltage and frequency to better fit the changing characteristics of the workload. Not only can a core be forced to a low power mode when its activity level is low, but the power saved by doing so could be opportunistically re-budgeted to other cores to boost the overall system throughput. To this end, we propose a holistic solution to energy efficiency improvement by seamlessly combining heterogeneity, Dynamic ResourceAllocation (DRA) and Dynamic Voltage and Frequency Adaptation (DVFA) capabilities to adapt the core resources to the changing demands of applications. Our results show that the proposed scheme provides anEPI reduction of about 17.9% when compared to the baseline heterogeneous multicore, 14% when compared to the baseline heterogeneous multicore with DVFA only and about 16.5% when compared to the baseline heterogeneous multicore with DRA only.

The computational needs of a program change over time. Sometimes a program exhibits low instruction level parallelism (ILP), while at other times the inherent ILP may be higher; sometimes a program stalls due to a large number of cache misses, while at other times it may exhibit high cache throughput. Asymmetric Multicore Processors (AMP) have been proposed to allow matching the computing needs of a thread to a core where it executes most efficiently. Some of the recent works focus on AMPs consisting of a monolithic large out-of-order (OOO) core and a small in-order (InO) core. Dynamic swapping of threads between these cores is then facilitated to improve energy efficiency of the threads without impacting performance too negatively. Swapping decisions are made at coarse grain instruction granularities to mitigate the impact of migration overhead. This excludes many opportunities for swap at a fine granular level. In this paper we consider a single superscalar OOO core that can morph itself dynamically into an InO core at runtime. In order to determine when to morph from OOO to InO and vice-versa, we rely on certain hardware performance monitors. Using these performance monitors we estimate the energy-delay-squared product (ED2P) for both modes of operation, which is then used to make morphing decisions. The morphing hardware support is simple and is already available in certain Intel processors to facilitate debug. The proposed scheme has low migration overhead, that enables fine-grain morphing to achieve more energy efficient computing by trading a small loss of performance for much greater energy reduction.

Asymmetric Multicore Processors (AMP) have emerged as likely candidates to solve the performance/power conundrum in the current generation of processors. Most recent work in this area evaluate such multicores by considering large (usually out-of-order (OOO)) and small (usually in-order (InO)) cores on the same chip. Dynamic online swapping of threads between these cores is then facilitated whenever deemed beneficial. However, if threads are swapped too often, the overheads may negatively impact the benefits of swapping. Hence, in most recent work, thread swapping decisions are made at coarse grain instruction granularities, leaving out many opportunities. In this paper, we propose a scheme to mitigate the penalty imposed by thread swapping and yet achieve all the benefits of AMPs. Here, a single superscalar OOO core morphs itself into an InO core at runtime, whenever determined to be performance/Watt efficient. Certain Intel processors already have a similar mechanism to statically morph an OOO core to an InO core to facilitate debug. We extend this existing capability to perform dynamic core morphing at runtime with an orthogonal objective of improving power efficiency. Results indicate that on an average, performance/Watt benefits of 10% can be extracted by our proposed morphing scheme at a very small performance penalty of 3.8%. Since this scheme is based on existing mechanisms readily available in current microprocessors, it incurs no hardware overheads.