Adaptive energy minimization of OpenMP parallel applications on many-core systems

Energy minimization of parallel applications is an emerging challenge for current and future generations of many-core computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement

Prognostic value of serum levels of immunoglobulins (IgG, IgA, IgM and IgE) in breast cancer: a preliminary study.

One hundred and sixty women admitted for breast tumour biopsy to the King's College Hospital group have been followed sequentially for 2 years. Sixty-nine women had early operable breast cancer and 91 had benign breast disease. All these women had serum immunoglobulin IgG, IgA, IgM and IgE levels measured preoperatively and postoperatively at 3 months, 1 year and 2 years. No differences were found in any of the serum immunoglobulin levels between the two groups at any time. There was, however, a positive correlation between the extent of metastatic breast cancer and the serum level of various immunoglobulins, particularly IgA. There was no evidence that routine postoperative radiotherapy influenced the levels of serum immunoglobulins. The findings suggest a secondary defence reaction against increasing tumour load, and do not support the theory of an early immune defect in immunoglobulin metabolism which could play a part in the pathogenesis of breast cancer. Although there is no diagnostic value in measuring the levels of serum immunoglobulins in patients with breast tumours, there may be some value in following the levels in cancer patients, as a guide to subclinical spread of the disease

Learning-based runtime management of energy-efficient and reliable many-core systems

This paper highlights and demonstrates our research works to date addressing the energy-efficiency and reliability challenges of many-core systems through intelligent runtime management algorithms. The algorithms are implemented through cross-layer interactions between the three layers: application, runtime and hardware, forming our core theme of working together. The annotated application tasks communicate the performance, energy or reliability requirements to the runtime. With such requirements, the runtime exercises the hardware through various control knobs and gets the feedback of these controls through the performance monitors. The aim is to learn the best possible hardware controls during runtime to achieve energy-efficiency and improved reliability, while meeting the specified application requirements

Thermal-aware adaptive energy minimization of open MP parallel applications

Energy minimization of parallel applications considering thermal distributions among the processor cores is an emerging challenge for current and future generations of many-core computing systems. This paper proposes an adaptive energy minimization approach that hierarchically applies dynamic voltage\slash frequency scaling (DVFS), thread-to-core affinity and dynamic concurrency controls (DCT) to address this challenge. The aim is to minimize the energy consumption and achieve balanced thermal distributions among cores, thereby improving the lifetime reliability of the system, while meeting a specified power budget requirement. Fundamental to this approach is an iterative learning-based control algorithm that adapts the VFS and core allocations dynamically based on the CPU workloads and thermal distributions of the processor cores, guided by the CPU performance counters at regular intervals. The adaptation is facilitated through modified OpenMP library-based power budget annotations. The proposed approach is extensively validated on an Intel Xeon E5-2630 platform with up to 12 CPUs running NAS parallel benchmark applications

Manipulating the antioxidant capacity of halophytes to increase their cultural and economic value through saline cultivation

Halophytes, salt-tolerant plants, are a source of valuable secondary metabolites with potential economic value. The steady-state pools of many stress-related metabolites are already enhanced in halophytes when compared with glycophytes, but growth under conditions away from the optimum can induce stress and consequently result in changes to secondary metabolites such as antioxidants. However, direct evidence for increasing the concentration of valuable secondary metabolites as a consequence of altering the salinity of the growing environment still remains equivocal. To address this, we analysed a range of metabolites with antioxidant capacity (including total phenols, flavonoids, ascorbate, reduced/oxidized glutathione and reactive oxygen species scavenging enzymes) in seedlings and plants from different families (Amaranthaceae, Brassicaceae, Plantaginaceae and Rhizophoraceae) and habitats grown under different salt concentrations. We show that it is possible to manipulate the antioxidant capacity of plants and seedlings by altering the saline growing environment, the length of time under saline cultivation and the developmental stage. Among the species studied, the halophytes Tripolium pannonicum, Plantago coronopus, Lepidium latifolium and Salicornia europaea demonstrated the most potential as functional foods or nutraceuticals.Deutsche Bundesstiftung Umwelt/AZ/27708COST/STSM/FA/0901-041011-011415DEFR

Learning transfer-based adaptive energy minimization in embedded systems

Embedded systems execute applications with different performance requirements. These applications exercise the hardware differently depending on the types of computation being carried out, generating varying workloads with time. We will demonstrate that energy minimization with such workload and performance variations within (intra) and across (inter) applications is particularly challenging. To address this challenge we propose an online energy minimization approach, capable of minimizing energy through adaptation to these variations. At the core of the approach is an initial learning through reinforcement learning algorithm that suitably selects the appropriate voltage/frequency scalings (VFS) based on workload predictions to meet the applications’ performance requirements. The adaptation is then facilitated and expedited through learning transfer, which uses the interaction between the system application, runtime and hardware layers to adjust the power control levers. The proposed approach is implemented as a power governor in Linux and validated on an ARM Cortex-A8 running different benchmark applications. We show that with intra- and inter-application variations, our proposed approach can effectively minimize energy consumption by up to 33% compared to existing approaches. Scaling the approach further to multi-core systems, we also show that it can minimize energy by up to 18% with 2X reduction in the learning time when compared with a recently reported approach