This paper presents CHeckpointed Early Resource RecYcling (Cherry), a hybrid mode of execution based on ROB and checkpointing that decouples resource recycling and instruction retirement. Resources are recycled early, resulting in a more efficient utilization. Cherry relies on state checkpointing and rollback to service exceptions for instructions whose resources have been recycled. Cherry leverages the ROB to (1) not require in-order execution as a fallback mechanism, (2) allow memory replay traps and branch mispredictions without rolling back to the Cherry checkpoint, and (3) quickly fall back to conventional out-of-order execution without rolling back to the checkpoint or flushing the pipeline.We present a Cherry implementation with early recycling at three different points of the execution engine: the load queue, the store queue, and the register file. We report average speedups of 1.06 and 1.26 in SPECint and SPECfp applications, respectively, relative to an aggressive conventional architecture. We also describe how Cherry and speculative multithreading can be combined and complement each other.
Although adaptive processors can exploit application variability to improve performance or save energy, effectively managing their adaptivity is challenging. To address this problem, we introduce a new approach to adaptivity: the Positional approach. In this approach, both the testing of configurations and the application of the chosen configurations are associated with particular code sections. This is in contrast to the currently-used Temporal approach to adaptation, where both the testing and application of configurations are tied to successive intervals in time.We propose to use subroutines as the granularity of code sections in positional adaptation. Moreover, we design three implementations of subroutine-based positional adaptation that target energy reduction in three different workload environments: embedded or specialized server, general purpose, and highly dynamic. All three implementations of positional adaptation are much more effective than temporal schemes. On average, they boost the energy savings of applications by 50% and 84% over temporal schemes in two experiments.
Using adaptive processing to dynamically tune major microprocessor resources, developers can achieve greater energy efficiency with reasonable hardware and software overhead while avoiding undue performance loss.
While technology is delivering increasingly sophisticated and powerful chip designs, it is also imposing alarmingly high energy requirements on the chips. One way to address this problem is to manage the energy dynamically. Unfortunately, current dynamic schemes for energy management are relatively limited. In addition, they manage energy either for energy efficiency or for temperature control, but not for both simultaneously.In this paper, we design and evaluate for the first time an energymanagement framework that tackles both energy efficiency and temperature control in a unified manner. We call this general approach Dynamic Energy Efficiency and Temperature Management (DEETM). Our framework combines many energy-management techniques and can activate them individually or in groups in a finegrained manner according to a given policy. The goal of the framework is two-fold: maximize energy savings without extending application execution time beyond a given tolerable limit, and guarantee that the temperature remains below a given limit while minimizing any resulting slowdown. The framework successfully meets these goals. For example, it delivers a 40% energy reduction with only a 10% application slowdown.
Abstract-As the number of cores in a chip multiprocessor (CMP) increases, the need for larger on-chip caches also increases in order to avoid creating a bottleneck at the off-chip interconnect. Utilization of these CMPs include combinations of multithreading and multiprogramming, showing a range of sharing behavior, from frequent inter-thread communication to no communication. The goal of the CMP cache design is to maximize capacity for a given size while providing as low a latency as possible for the entire range of sharing behavior.In a typical CMP design, the last level cache (LLC) is shared across the cores and incurs a latency of access that is a function of distance on the chip. Sharing helps avoid the need for replicas at the LLC and allows access to the entire on-chip cache space by any core. However, the cost is the increased latency of communication based on where data is mapped on the chip. In this paper, we propose a cache coherence design we call POPS that provides localized data and metadata access for both shared data (in multithreaded workloads) and private data (predominant in multiprogrammed workloads). POPS achieves its goal by (1) decoupling data and metadata, allowing both to be delegated to local LLC slices for private data and between sharers for shared data, (2) freeing delegated data storage in the LLC for larger effective capacity, and (3) changing the delegation and/or coherence protocol action based on the observed sharing pattern.Our analysis on an execution-driven full system simulator using multithreaded and multiprogrammed workloads shows that POPS performs 42% (28% without microbenchmarks) better for multithreaded workloads, 16% better for multiprogrammed workloads, and 8% better when one single-threaded application is the only running process, compared to the base non-uniform shared L2 protocol. POPS has the added benefits of reduced on-chip and off-chip traffic and reduced dynamic energy consumption.
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