\u3cem\u3eHP-DAEMON\u3c/em\u3e: \u3cem\u3eH\u3c/em\u3eigh \u3cem\u3eP\u3c/em\u3eerformance \u3cem\u3eD\u3c/em\u3eistributed \u3cem\u3eA\u3c/em\u3edaptive \u3cem\u3eE\u3c/em\u3energy-efficient \u3cem\u3eM\u3c/em\u3eatrix-multiplicati\u3cem\u3eON\u3c/em\u3e

The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than a classic strategy. Moreover, we leverage a high performance communication scheme for fully exploiting network bandwidth via pipeline broadcast. Overall, the integrated approach achieves substantial energy savings (up to 51.4%) and performance gain (28.6% on average) compared to ScaLAPACK pdgemm() on a cluster with an Ethernet switch, and outperforms ScaLAPACK and DPLASMA pdgemm() respectively by 33.3% and 32.7% on average on a cluster with an Infiniband switch

Memory hierarchy reconfiguration for energy and performance in general-purpose processor architectures

Journal ArticleConventional microarchitectures choose a single memory hierarchy design point targeted at the average application. In this paper we propose a cache and TLB layout and design that leverages repeater insertion to provide dynamic low-cost configurability trading of size and speed on a per application phase basis. A novel configuration management algorithm dynamically detects phase changes and reacts to an application's hit and miss intolerance in order to improve memory hierarchy performance while taking energy consumption into consideration. When applied to a two-level cache and TLB hierarchy at O.Ipm technology, the result is an average 15% reduction in cycles per instruction (CPI), corresponding to an average 27% reduction in memory-CPI, across a broad class of applications compared to the best conventional two-level hierarchy of comparable size. Projecting to sub-. I pm technology design considerations that call for a three-level conventional cache hierarchy for performance reasons, we demonstrate that a configurable L2/L3 cache hierarchy coupled with a conventional LI results in an average 43% reduction in memory hierarchy energy in addition to improved performance

Inter-cluster Thread-to-core Mapping and DVFS on Heterogeneous Multi-cores

Heterogeneous multi-core platforms that contain different types of cores, organized as clusters, are emerging, e.g. ARM's big.LITTLE architecture. These platforms often need to deal with multiple applications, having different performance requirements, executing concurrently. This leads to generation of varying and mixed workloads (e.g. compute and memory intensive) due to resource sharing. Run-time management is required for adapting to such performance requirements and workload variabilities and to achieve energy efficiency. Moreover, the management becomes challenging when the applications are multi-threaded and the heterogeneity needs to be exploited. The existing run-time management approaches do not efficiently exploit cores situated in different clusters simultaneously (referred to as inter-cluster exploitation) and DVFS potential of cores, which is the aim of this paper. Such exploitation might help to satisfy the performance requirement while achieving energy savings at the same time. Therefore, in this paper, we propose a run-time management approach that first selects thread-to-core mapping based on the performance requirements and resource availability. Then, it applies online adaptation by adjusting the voltage-frequency (V-f) levels to achieve energy optimization, without trading-off application performance. For thread-to-core mapping, offline profiled results are used, which contain performance and energy characteristics of applications when executed on the heterogeneous platform by using different types of cores in various possible combinations. For an application, thread-to-core mapping process defines the number of used cores and their type, which are situated in different clusters. The online adaptation process classifies the inherent workload characteristics of concurrently executing applications, incurring a lower overhead than existing learning-based approaches as demonstrated in this paper. The classification of workload is performed using the metric Memory Reads Per Instruction (MRPI). The adaptation process pro-actively selects an appropriate V-f pair for a predicted workload. Subsequently, it monitors the workload prediction error and performance loss, quantified by instructions per second (IPS), and adjusts the chosen V-f to compensate. We validate the proposed run-time management approach on a hardware platform, the Odroid-XU3, with various combinations of multi-threaded applications from PARSEC and SPLASH benchmarks. Results show an average improvement in energy efficiency up to 33% compared to existing approaches while meeting the performance requirements

Peer-to-Peer EnergyTrade: A Distributed Private Energy Trading Platform

Blockchain is increasingly being used as a distributed, anonymous, trustless framework for energy trading in smart grids. However, most of the existing solutions suffer from reliance on Trusted Third Parties (TTP), lack of privacy, and traffic and processing overheads. In our previous work, we have proposed a Secure Private Blockchain-based framework (SPB) for energy trading to address the aforementioned challenges. In this paper, we present a proof-on-concept implementation of SPB on the Ethereum private network to demonstrates SPB's applicability for energy trading. We benchmark SPB's performance against the relevant state-of-the-art. The implementation results demonstrate that SPB incurs lower overheads and monetary cost for end users to trade energy compared to existing solutions