As one of the promising efforts to minimize the surging microprocessor power consumption, adaptive computing environments (ACEs), where microarchitectural resources can be dynamically tuned to match a program's run-time requirement and characteristics, are becoming increasingly common. In an ACE, efficient management of the configurable units (CUs) is vital for maximizing the benefit of resource adaptation. ACEs usually have multiple configurable hardware units, necessitating exploration of a large number of combinatorial configurations in order to identify the most energy-efficient configuration. In this paper, we propose an ACE management framework for efficient management of multiple CUs, utilizing dynamic optimization systems' inherent capabilities of detecting and optimizing program hotspots, i.e., dominate code regions. We develop a scheme where hotpot boundaries are used for phase detection and adaptation. The framework achieves good energy reduction on managing multiple CUs with minimal hardware requirements and low implement cost by leveraging the existing infrastructure of a dynamic optimization system. The proposed framework is evaluated by dynamically adapting five CUs with distinct reconfiguration latencies and overheads. Those CUs are issue queue, reorder buffer, level-one data and instruction caches, and level-two cache. Previous research indicates that those five components dominate the energy consumption of a microprocessor. Despite the growing complexity and overhead of adapting five CUs, our technique reduces the energy consumption of those CUs by as much as 45%, while one of the best techniques provided by prior literature achieves less than 15% energy reduction for all CUs.

This paper develops a technique that uniquely combines the advantages of compile-time static scheduling and hardware dynamic scheduling to reduce energy consumption in dynamically scheduled processors. In this hybrid-scheduling paradigm, regions of the application containing large amounts of parallelism visible at compile-time bypass the dynamic scheduling hardware and execute in a low-power static mode. Experiments on several media and scientific benchmarks demonstrate that the proposed scheme can provide significant reduction in energy consumption with negligible performance degradation

To minimize the surging power consumption of microprocessors, adaptive computing environments (ACEs) where microarchitectural resources can be dynamically tuned to match a program's runtime requirement and characteristics are becoming increasingly common. Adaptive computing environments usually have multiple configurable hardware units, necessitating exploration of a large number of combinatorial configurations in order to identify the most energy-efficient configuration. In this paper, we propose a scheme for efficient management of multiple configurable units, utilizing the inherent capabilities of dynamic optimization systems. Most dynamic optimizers typically detect dominant code regions (hotspots). We develop an ACE management scheme where hotpot boundaries are used for phase detection and adaptation. Since hotspots are of variable sizes and are often nested, program phase behavior which is hierarchical in nature is automatically captured in this technique. To demonstrate the usefulness and effectiveness of our framework, we use the proposed framework to dynamically adapt the sizes of L1 data and L2 caches that have different reconfiguration latencies and overheads. Our technique reduces L1D and L2 cache energy consumption by 47% and 58%, while a popular previously proposed technique only achieves reduction of 32% and 52% respectively.

In an out-of-order issue processor, instructions are dynamically reordered and issued to function units in their data-ready order rather than their original program order to achieve high performance. The logic that facilitates dynamic issue is one of the most power-hungry and time-critical components in a typical out-of-order issue processor.This paper develops a cooperative hardware/software technique to reduce complexity and energy consumption of the issue logic. The proposed scheme is based on the observation that not all instructions in a program require the same amount of dynamic reordering. Instructions that belong to basic blocks for which the compiler can perform near-optimal sche- duling do not need any intra-block instruction reordering but require only inter-block instruction overlap. In contrast, blocks where the compiler is limited by artificial dependences and memory misses require both intra-block and inter-block instruction reordering. The proposed Reorder-Sensitive Issue Scheme utilizes a novel compile-time analyzer to evaluate the quality of schedules generated by the static scheduler and to estimate the dynamic reordering requirement of instructions within each basic block. At the micro-architecture-level, we propose a novel issue queue that exploits the varying dynamic scheduling requirement of basic blocks to lower the power dissipation and complexity of the dynamic issue hardware.An evaluation of the technique on several SPEC integer benchmarks indicates that we can reduce the energy consumption in the issue queue on average by 72% with only 5% performance degradation Additionally, the proposed issue hardware is significantly less complex when compared to a conventional monolithic out-of-order issue queue, providing the potential for high clock speeds.

This paper develops a technique that uniquely combines the advantages of static scheduling and dynamic scheduling to reduce the energy consumed in modern superscalar processors with out-of-order issue logic. In this Hybrid-Scheduling paradigm, regions of the application containing large amounts of parallelism visible at compile-time completely bypass the dynamic scheduling logic and execute in a low power static mode. Simulation studies using the Wattch framework on several media and scientific benchmarks demonstrate large improvements in overall energy consumption of 43% in kernels and 25% in full applications with only a 2.8% performance degradation on average.

Modulo scheduling is an aggressive scheduling technique for loops that exploit instruction-level parallelism by overlapping successive iterations of the loop. Due to the nature of modulo scheduling, the lifetime of a variable can overlap with a subsequent definition of itself. To handle such overlapping lifetimes, modulo-variable expansion (MVE) is used, wherein the constructed schedule is unrolled a number of times. We propose a technique to improve the constructed schedule while performing MVE. In our approach, we unroll the data dependence graph of the original loop and re-schedule it with a MVE-sensitive scheduler. Such an approach is expected to result in better initiation rates as compared to the traditional approach. We have implemented our approach and evaluated its performance on a large number of scientific benchmark kernels.