@article {1199,
	title = {Investigating Power Capping toward Energy-Efficient Scientific Applications},
	journal = {Concurrency Computation: Practice and Experience},
	volume = {2018},
	year = {2018},
	month = {2018-04},
	pages = {1-14},
	abstract = {The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system{\textquoteright}s power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. We quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.},
	keywords = {energy efficiency, High Performance Computing, Intel Xeon Phi, Knights landing, papi, performance analysis, Performance Counters, power efficiency},
	doi = {https://doi.org/10.1002/cpe.4485},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4485},
	author = {Azzam Haidar and Heike Jagode and Phil Vaccaro and Asim YarKhan and Stanimire Tomov and Jack Dongarra}
}
@conference {765,
	title = {Heterogeneous Acceleration for Linear Algebra in Mulit-Coprocessor Environments},
	booktitle = {VECPAR 2014},
	year = {2014},
	month = {2014-06},
	address = {Eugene, OR},
	abstract = {We present an efficient and scalable programming model for the development of linear algebra in heterogeneous multi-coprocessor environments. The model incorporates some of the current best design and implementation practices for the heterogeneous acceleration of dense linear algebra (DLA). Examples are given as the basis for solving linear systems{\textquoteright} algorithms {\textendash} the LU, QR, and Cholesky factorizations. To generate the extreme level of parallelism needed for the efficient use of coprocessors, algorithms of interest are redesigned and then split into well-chosen computational tasks. The tasks execution is scheduled over the computational components of a hybrid system of multi-core CPUs and coprocessors using a light-weight runtime system. The use of light-weight runtime systems keeps scheduling overhead low, while enabling the expression of parallelism through otherwise sequential code. This simplifies the development efforts and allows the exploration of the unique strengths of the various hardware components.},
	keywords = {Computer science, factorization, Heterogeneous systems, Intel Xeon Phi, linear algebra},
	author = {Azzam Haidar and Piotr Luszczek and Stanimire Tomov and Jack Dongarra}
}
@conference {809,
	title = {Unified Development for Mixed Multi-GPU and Multi-Coprocessor Environments using a Lightweight Runtime Environment},
	booktitle = {IPDPS 2014},
	year = {2014},
	month = {2014-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Phoenix, AZ},
	abstract = {Many of the heterogeneous resources available to modern computers are designed for different workloads. In order to efficiently use GPU resources, the workload must have a greater degree of parallelism than a workload designed for multicore-CPUs. And conceptually, the Intel Xeon Phi coprocessors are capable of handling workloads somewhere in between the two. This multitude of applicable workloads will likely lead to mixing multicore-CPUs, GPUs, and Intel coprocessors in multi-user environments that must offer adequate computing facilities for a wide range of workloads. In this work, we are using a lightweight runtime environment to manage the resourcespecific workload, and to control the dataflow and parallel execution in two-way hybrid systems. The lightweight runtime environment uses task superscalar concepts to enable the developer to write serial code while providing parallel execution. In addition, our task abstractions enable unified algorithmic development across all the heterogeneous resources. We provide performance results for dense linear algebra applications, demonstrating the effectiveness of our approach and full utilization of a wide variety of accelerator hardware.},
	keywords = {algorithms, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, linear algebra, nVidia, Tesla K20, Tesla M2090},
	author = {Azzam Haidar and Chongxiao Cao and Jack Dongarra and Piotr Luszczek and Stanimire Tomov}
}
