Improve Application Performance Research Articles

Barkhausen noise analysis of thin film ferroelectrics

The first direct Barkhausen noise measurement in a ferroelectric thin film is presented. The Barkhausen noise energy loop is reconstructed from the measured Barkhausen noise and is closely related to the classic ferroelectric P vs E hysteresis loop. Grain boundaries act as a dominant ferroelectric domain wall pinning site in a polycrystalline thin film based on the calculated domain wall jump distance using the Barkhausen noise frequency. The technique is promising for the measurement of ferroelectric switching dynamics, and provides a physical insight for improving application performance.

Laser-induced damage in a silicon-based photodiode by MHz picosecond laser

Study of the damage characteristics of silicon-based photodetectors is of great significance in improving application performance, but few studies of laser-induced damage have been undertaken on photodetectors irradiated by hundred-ps fiber lasers. In this work, we demonstrate an all-fiber ps-pulsed laser system which outputs a pulse width of 226.5 ps and an average power of 20 W at 2.4 MHz repetition rate, with high beam quality and pulse stability. Experiments use this fiber amplifier to irradiate a silicon-based positive–intrinsic–negative (PIN) photodiode. The changes in detector performance and surface morphology are confirmed by systematic experiments on the laser-irradiated detector. The results show that detection performance was sensitive to laser energy, and the failure damage threshold was about 864 kW cm−2. Based on hydrodynamics theory, we simulated the laser-induced plasma density distribution in a PIN photodiode and studied the thermal and plasma damage mechanisms in the PIN photodiode.

Latency Imbalance Among Internet Load-Balanced Paths

Load balancers choose among load-balanced paths to distribute traffic as if it makes no difference using one path or another. This work shows that the latency difference between load-balanced paths (called latency imbalance ), previously deemed insignificant, is now prevalent from the perspective of the cloud and affects various latency-sensitive applications. In this work, we present the first large-scale measurement study of latency imbalance from a cloud-centric view. Using public cloud around the globe, we measure latency imbalance both between data centers (DCs) in the cloud and from the cloud to the public Internet. Our key findings include that 1) Amazon's and Alibaba's clouds together have latency difference between load-balanced paths larger than 20ms to 21% of public IPv4 addresses; 2) Google's secret in having lower latency imbalance than other clouds is to use its own well-balanced private WANs to transit traffic close to the destinations and that 3) latency imbalance is also prevalent between DCs in the cloud, where 8 pairs of DCs are found to have load-balanced paths with latency difference larger than 40ms. We further evaluate the impact of latency imbalance on three applications (i.e., NTP, delay-based geolocation and VoIP) and propose potential solutions to improve application performance. Our experiments show that all three applications can benefit from considering latency imbalance, where the accuracy of delay-based geolocation can be greatly improved by simply changing how \textttping measures the minimum path latency.

A perspective on graphene for supercapacitors: Current status and future challenges

This perspective highlights the uniqueness of graphene for supercapacitors and discusses the challenges and opportunities in practical application and further performance improvement of graphene-based supercapacitors.

Experimental assessment of heat transfer and pressure drop of nanofluid as a coolant in corrugated channels

Over the last few decades, tremendous consideration is drawn towards corrugation surfaces because of their advantages over the improvement in thermal performance for different engineering applications. An experimental investigation is carried out to compare the effects of combined corrugated walls and turbulent nanofluid flow on thermo-hydraulic performance in corrugated channels over Reynolds number ranges of 10,000–30,000 and constant heat flux of 1 × 104 W m−2. Three shapes, namely semicircle corrugated channel, trapezoidal corrugated channel (TCC), and straight channel, are fabricated and tested with 1% and 2% volume fraction of Al2O3–water nanofluids. Al2O3 nanoparticles suspended in water with two volume fractions ( $$\phi$$ ) of 1.0% and 2.0% are successfully prepared and tested. The experimental findings demonstrate that employing corrugated channel (TCC) improves heat transfer levels by up to 63.59%, pressure drop by 1.37 times, and thermal performance by up to 2.22 times compared to straight channels. Furthermore, heat transfer increased as Al2O3’s volume fraction increases due to the thermal conductivity boost. The use of the tested channels and alumina nanofluid at a volume fraction of 2.0% caused an increase in the heat transfer ratio of around 7.9–8.3% compared to the utilization of the same channels with base fluid. New empirical correlations of corrugated channels with alumina nanofluid are also developed and reported for heat transfer applications.

Hotspot-Aware Hybrid Memory Management for In-Memory Key-Value Stores

Emerging Non-Volatile Memory (NVM) technologies promise much higher memory density and energy efficiency than DRAM, at the expense of higher read/write latency and limited write endurance. Hybrid memory systems composed of DRAM and NVM have the potential to provide very large capacity of main memory for in-memory key-value (K-V) stores. However, there remains challenges to directly deploy DRAM-based K-V stores in hybrid memory systems. The performance and energy efficiency of K-V stores on hybrid memory systems have not been fully explored yet. In this paper, we propose HMCached , an in-memory K-V store built on a hybrid DRAM/NVM system. HMCached utilizes an application-level data access counting mechanism to identify frequently-accessed (hotspot) objects (i.e., K-V pairs) in NVM, and migrates them to fast DRAM to reduce the costly NVM accesses. We also propose an NVM-friendly index structure to store the frequently-updated portion of object metadata in DRAM, and thus further mitigate the NVM accesses. Moreover, we propose a benefit-aware memory reassignment policy to address the slab calcification problem in slab-based K-V store systems, and significantly improve the benefit gain from the DRAM. We implement the proposed schemes with Memcached and evaluate it with Zipfian-like workloads. Experiment results show that HMCached significantly reduces NVM accesses by 70 percent compared to the vanilla Memcached running on a DRAM/NVM hybrid memory system without any optimizations, and improves application performance by up to 50 percent. Moreover, compared to a DRAM-only system, HMCached achieves 90 percent of performance and 46 percent reduction of energy consumption for realistic (read-intensive) workloads while significantly reducing the DRAM usage by 75 percent.

A Non-Uniform Broadcast Fertilization Method and Its Performance Analysis under Basin Irrigation

The broadcast fertilization method is widely used under basin irrigation in China. A reasonable broadcast fertilization method can effectively improve application performance of fertilization and reduce pollution from non-point agricultural sources. In this study, firstly, a non-uniform broadcast fertilization method and a non-uniform application coefficient were proposed. The value of non-uniform application coefficient is defined in this paper. It represents the ratio of the difference between the maximum and the average fertilization amount of fertilizer applied on the basin surface to the average fertilization amount of fertilizer applied on the basin surface. Secondly, field experiments were conducted to study the movement characteristics of fertilizer under non-uniform broadcast fertilization for basin irrigation. Field experiment results showed that under the condition of basin irrigation, the non-uniform broadcast fertilization method could weaken the non-uniform distribution of fertilizer due to erosion and transport capacity of solid fertilizer by irrigation water flow, which could significantly improve the uniformity of soil solute content. Thirdly, the solute transport model for broadcast fertilization was corroborated by the field experiment results. The variation rule of fertilization performance with non-uniform application coefficient under different basin length and inflow rate was achieved by simulation. The simulation results showed that fertilization uniformity and fertilization storage efficiency increased first and then decreased with the increase of non-uniform application coefficient. In order to be practically applicable, suitable irrigation programs of non-uniform application coefficient under different basin length and inflow rate conditions were proposed by numerical simulation.

SimAS: A simulation‐assisted approach for the scheduling algorithm selection under perturbations

SummaryMany scientific applications consist of large and computationally intensive loops. Dynamic loop self‐scheduling (DLS) techniques are used to parallelize and to balance the load of such applications during execution. Load imbalance arises from variations in the loop iteration (or tasks) execution times, caused by problem, algorithmic, or systemic characteristics. Variations in systemic characteristics are referred to as perturbations. Our hypothesis is that no single DLS technique can achieve the absolute best performance under various perturbations on heterogeneous high‐performance computing (HPC) systems. Therefore, the selection of the most efficient DLS technique is critical to achieve the best application performance. The goal of this work is to solve the algorithm selection problem for the scheduling of computationally intensive applications under perturbations. Existing work only considers perturbations caused by variations in the delivered computational speed of the HPC systems. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. A simulation‐assisted scheduling algorithm selection (SimAS) approach is introduced herein as a novel control‐theoretic‐inspired approach to select DLS techniques dynamically that improve the performance of applications executing on heterogeneous HPC systems under perturbations. The present work examines the performance of seven applications on a heterogeneous HPC system under all the above system perturbations. SimAS is evaluated using native and simulative experiments. The performance results confirm the original hypothesis that motivates this work. The experimental evaluation shows that the SimAS‐based DLS selection identifies the most efficient technique and improves application performance in most cases.

TLB Coalescing for Multi-Grained Page Migration in Hybrid Memory Systems

Superpages have long been proposed to enlarge the coverage of translation lookaside buffer (TLB). They are extremely beneficial for reducing address translation overhead in big memory systems, such as hybrid memory systems that composed of DRAM and non-volatile memories (NVMs). However, superpages conflict with fine-grained memory migration, one of the key techniques in hybrid memory systems to improve performance and energy efficiency. Fine-grained page migrations usually require to splinter superpages, mitigating the benefit of TLB hardware for superpages. In this paper, we present Tamp, an efficient memory management mechanism to support multiple page sizes in hybrid memory systems. We manage large-capacity NVM using superpages, and use a relatively small size of DRAM to cache hot base pages within the superpages. We find that there are remarkable contiguity exist for hot base pages in superpages. In response, we bind those contiguous hot pages together and migrate them to DRAM. We also propose multi-grained TLBs to coalesce multiple page address translations into a single TLB entry. Our experimental results show that Tamp can significantly reduce TLB misses by 62.4% on average, and improve application performance (IPC) by 16.2%, compared to a page migration policy without TLB coalescing support.

Mapping Spiking Neural Networks to Neuromorphic Hardware

Neuromorphic hardware implements biological neurons and synapses to execute a spiking neural network (SNN)-based machine learning. We present SpiNeMap, a design methodology to map SNNs to crossbar-based neuromorphic hardware, minimizing spike latency and energy consumption. SpiNeMap operates in two steps: SpiNeCluster and SpiNePlacer. SpiNeCluster is a heuristic-based clustering technique to partition an SNN into clusters of synapses, where intracluster local synapses are mapped within crossbars of the hardware and intercluster global synapses are mapped to the shared interconnect. SpiNeCluster minimizes the number of spikes on global synapses, which reduces spike congestion and improves application performance. SpiNePlacer then finds the best placement of local and global synapses on the hardware using a metaheuristic-based approach to minimize energy consumption and spike latency. We evaluate SpiNeMap using synthetic and realistic SNNs on a state-of-the-art neuromorphic hardware. We show that SpiNeMap reduces average energy consumption by 45% and spike latency by 21%, compared to the best-performing SNN mapping technique.

Thermal characteristics, kinetics, gas emissions and thermodynamic simulations of (co-)combustions of textile dyeing sludge and waste tea

Abstract The thermal conversion of waste into energy is increasingly becoming an integral part of environmentally friendly and sustainable societies. In this study, the (co-)combustion behaviors, kinetics, and gas emissions of textile dyeing sludge (TDS) and waste tea (WT) were quantified. The addition of WT appeared to avoid the drawbacks of both TDS and WT and to enhance their combustion efficiency. The main co-combustion process was characterized by three stages. The WT addition led to higher reactivity and a better combustion performance. The average apparent activation energy reached its minimum (154.82 kJ/mol) with 40% WT. The reaction mechanisms of the three stages of the 40% WT blend were best described using the D2, F3 and F2.3 models, respectively. The interaction between TDS and WT occurred between 370 and 550 °C. The WT addition changed the peak strength of Fe2O3 and produced NaAlSiO4 and CaSO4. The blend ash composition was found to consist of Fe2O3, CaSO4, Na2SO4, Ca5HP3O13, and NaAlSiO4 according to X-ray diffraction analysis and thermodynamic simulations. The WT addition reduced SO2 emission from the co-combustion. Our results can be benefited to provide pollution reduction, energy generation, performance improvement, scaling-up, and optimization for the industrial applications.

Dual metal Schottky barrier asymmetric gate stack cylindrical gate all around (DM-SB-ASMGS-CGAA) MOSFET for improved analog performance for high frequency application

In this paper dual metal Schottky barrier asymmetric gate stack cylindrical gate all around (DM-SB-ASMGS-CGAA) MOSFET is analyzed for improvement in analog performance for applications with high frequency, using ATLAS 3D device simulator. This device is compared with conventional Schottky barrier MOSFET (SB-CGAA), Schottky barrier graded stack MOSFET (SBGS-CGAA) and dual metal graded channel stack Schottky barrier MOSFET (DMSBGS-CGAA) to analyze its analog performance and applications. It is observed that DM-SB-ASMGS MOSFET shows better performance in terms of high drain current, high transconductance, gain obtained from current (CG) and gain obtained from maximum transducer power (MTPG). Early voltage and intrinsic gain are desirable for the proposed device compared to the other devices showing its capability for high-gain amplification applications. The potential and electric field results are better for the proposed device compared to the previous device architectures. The subthreshold slope obtained for DM-SB-ASMGS MOSFET is 67 (mV/decade) which is lowest among the other comparable devices and so it is highly desirable for high frequency applications. Small signal AC analysis is also carried out in order to affirm with the analog performance results. The scattering parameters (S-parameters) are taken in account and shows that it gives better performance compared to its counterparts. The reflection coefficients that are S11, S22 show better matching to ports or minimum reflection. Also the parameters S12, S21 show higher voltage gains implying that the device can be used for high gains.

Multiple Active Sites of Carbon for High-Rate Surface-Capacitive Sodium-Ion Storage.

Although sodium ion batteries (SIBs) possess many beneficial features, their rate performance, cycling stability, and safety need improvement for commercial applications. Based on the mechanisms of the sodium ions storage in carbon materials, herein we present a multiple active sites decorated amorphous carbon (MAC) with rich structural defects and heteroatom doping as an anode material for SIBs. The full utilization of fast bonding-debonding processes between the active sites and sodium ions could bring a capacitive strategy to achieve superior sodium storage properties. Consequently, after materials characterization and electrochemical evaluation, the as-prepared electrode could deliver high rate and long-life performance. This active-site-related design could be extended to other types of electrode materials, thereby contributing to future practical SIB applications.

Multiple Active Sites of Carbon for High‐Rate Surface‐Capacitive Sodium‐Ion Storage

AbstractAlthough sodium ion batteries (SIBs) possess many beneficial features, their rate performance, cycling stability, and safety need improvement for commercial applications. Based on the mechanisms of the sodium ions storage in carbon materials, herein we present a multiple active sites decorated amorphous carbon (MAC) with rich structural defects and heteroatom doping as an anode material for SIBs. The full utilization of fast bonding–debonding processes between the active sites and sodium ions could bring a capacitive strategy to achieve superior sodium storage properties. Consequently, after materials characterization and electrochemical evaluation, the as‐prepared electrode could deliver high rate and long‐life performance. This active‐site‐related design could be extended to other types of electrode materials, thereby contributing to future practical SIB applications.

HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers

Reducing energy consumption in cloud data centers is one of the prime issue in the cloud community. It reduces energy related costs and increases lifespan of high performance computing resources deployed in cloud data centers and it also helps in reducing carbon emissions. Along with energy efficiency, problem of task scheduling is also one of the important problem considered in cloud data centers and it belongs to NP-class problems. With the energy consumption consideration, problem of task scheduling becomes more complex to solve. Metaheuristic algorithms are proven to generate near optimal solutions for task scheduling problem but their scheduling overhead increases vastly as the number of tasks or number of resources increases. In addition to this, metaheuristic-based scheduling algorithms lives most of time in local-optimal region of solution space and it is possible that it can finalizes the solution in the local-optimal region only. Primary motivation behind this work is from genetic algorithm itself, we have experimented with genetic algorithm in many ways to solve the problem of energy efficient task scheduling and come up with new hybrid scheme to solve it. In this work, we tackles the problem of energy efficient task scheduling on modern cloud data center architecture and proposes a novel hybrid metaheuristic scheme harmony-inspired genetic algorithm (HIGA). It also addresses issues associated with metaheuristic algorithm. HIGA combines the exploration capability of genetic algorithm and exploitation capability of harmony search by which it intelligently senses local as well as global optimal region without wasting time (iterations) in local or global optimal region and provides quick convergence. Our primary objectives in this work are to reduce makespan and computing energy and secondary objectives are to reduce the energy consumed by the resources other than computing resources and reduce execution overhead associated with scheduler. Collectively these objectives guides HIGA for better energy efficiency and performance while reducing the number of required resources (i.e. active racks). It indirectly also reduces cooling energy as we can switch off the rack components (air blower, cooling inlets) once racks become idle. Simulation analysis has been performed over independent task applications as well as real-world scientific applications like CyberShake, Epigenomics and Montage. The result clearly manifests that proposed HIGA provides by up to 33% of energy savings and 47% of improvement in application performance (makespan) that too with 39% less execution overhead.

HAWS

Graphics Processing Units (GPUs) have become an attractive platform for accelerating challenging applications on a range of platforms, from High Performance Computing (HPC) to full-featured smartphones. They can overcome computational barriers in a wide range of data-parallel kernels. GPUs hide pipeline stalls and memory latency by utilizing efficient thread preemption. But given the demands on the memory hierarchy due to the growth in the number of computing cores on-chip, it has become increasingly difficult to hide all of these stalls. In this article, we propose a novel Hint-Assisted Wavefront Scheduler (HAWS) to bypass long-latency stalls. HAWS starts by enhancing a compiler infrastructure to identify potential opportunities that can bypass memory stalls. HAWS includes a wavefront scheduler that can continue to execute instructions in the shadow of a memory stall, executing instructions speculatively, guided by compiler-generated hints. HAWS increases utilization of GPU resources by aggressively fetching/executing speculatively. Based on our simulation results on the AMD Southern Islands GPU architecture, at an estimated cost of 0.4% total chip area, HAWS can improve application performance by 14.6% on average for memory intensive applications.

Supporting Superpages and Lightweight Page Migration in Hybrid Memory Systems

Superpages have long been used to mitigate address translation overhead in large-memory systems. However, superpages often preclude lightweight page migration, which is crucial for performance and energy efficiency in hybrid memory systems composed of DRAM and non-volatile memory (NVM). In this article, we propose a novel memory management mechanism called Rainbow to bridge this fundamental conflict between superpages and lightweight page migration. Rainbow manages NVM at the superpage granularity, and uses DRAM to cache frequently accessed (hot) small pages within each superpage. Correspondingly, Rainbow utilizes split TLBs to support different page sizes. By introducing an efficient hot page identification mechanism and a novel NVM-to-DRAM address remapping mechanism, Rainbow supports lightweight page migration without splintering superpages. Experiment results show that Rainbow can significantly reduce applications’ TLB misses by 99.9%, and improve application performance (in terms of IPC) by up to 2.9× (45.3% on average) when compared to a state-of-the-art memory migration policy without a superpage support.

Fairness in dataflow scheduling in the cloud

Expensive dataflow queries which may involve large-scale computations operating on significant volumes of data are typically executed on distributed platforms to improve application performance. Among these, cloud computing has emerged as an attractive option for users to execute dataflows allowing them to select proper configurations (e.g., number of machines) to achieve desired trade-offs between execution time and monetary cost. Discovering dataflow schedules that exhibit the best trade-offs within a plethora of potential solutions can be challenging, especially in a heterogeneous environment where resource characteristics like performance and price can be varied. To increase resource utilization, users may also submit multiple dataflows for execution concurrently. Traditionally, building fair schedules (schedules where the slowdown of all dataflows due to resource sharing is similar) while achieving good performance is a major concern. However, considering fairness in the cloud computing setting where monetary cost is part of the optimization objectives significantly increases the difficulty of the scheduling problem. This paper proposes an algorithm for the scheduling of multiple dataflows on heterogeneous clouds that identifies Pareto-optimal solutions (schedules) in the three-dimensional space formed from the different trade-offs between overall execution time, monetary cost and fairness. The results show that in most cases the proposed approach can provide solutions with fairer schedules without significantly impacting the quality of the execution time to monetary cost skyline compared to the state of the art where the fairness of a solution is not taken into account.

A best-fit routing algorithm for non-redundant communication in large-scale IoT based network

In this manuscript a self-disciplined path discovery and information transmitting routing is proposed for Internet of Things (IoT) enabled applications. This routing process works at device level for self-healing and organizing its communication routes in a large-scale network. The routing process is opportunistic in nature, requiring local device information other than global update. This opportunistic routing (OR) is based on best-fit traversing (BFT) algorithm to improve the accessibility of the device in a precise manner. The best-fit algorithm discovers flawless neighbors for resuming interrupted communications in prolonged manner. The opportunistic local information based searching process avoids frequent disconnection and redundancy that is common in a single device multi-application scenario. The local route searching process is constructed using a threshold metric to confine the greedy behavior of the devices at the time of neighbor discovery. The routing process is designed to support network scalability to adapt its benefits in the varying network dimension. The proposed opportunistic routing technique improves application performance by minimizing transmission delay, redundancy and increasing communication rates.

Detecting Bogus Information Attack in Vehicular Ad Hoc Network: A Context-Aware Approach

Most of Vehicular Ad Hoc Network’s (VANET) and Fly Ad Hoc Network (FANET) applications, protocols, and service rely on cooperativeness among network participants. Vehicles, could be unmanned aerial vehicles (e.g. drones), share their sensors’ information to improve network performance and provide safety, traffic efficiency, and entertainments. The accuracy and reliability of this information are vital for VANET performance. However, misbehaving (or faulty) vehicles, which exploit the cooperativeness feature by sharing bogus information, can disrupt the fundamental operations of any potential application and cause loss of people lives and properties. Unfortunately, existing detection approaches cannot effectively thwart such attacks. The highly dynamic vehicular context has been vastly neglected as well as they rely on predefined and static security perimeters to differentiate between false and correct information. In this paper, a context-aware detection approach is proposed to improve the detection accuracy and reduce the false alarm rate. Firstly, the features that well represent the vehicular context has been extracted. Then, an online unsupervised learning method namely the hierarchical clustering algorithm is used to classify the received messages into either genuine or spurious. Finally, Bayesian-based hypothesis testing has been used to confirm the validity of the classification. Results show that the proposed detection approach is promising in effectively detect bogus information attack and improve applications performance.