Quality Assessment of GPU Power Profiling Mechanisms

doi:https://doi.org/10.1109/IPDPSW.2018.00113

Title: Quality Assessment of GPU Power Profiling Mechanisms

Full Record
Other Related Research

Abstract

Accurate component-level power measurements are nowadays essential for the design and optimization of high-performance computing (HPC) systems and applications. Particularly, as more and more heterogeneous HPC systems are developed, the characterizations of GPU power profiles have become extremely crucial because, although GPUs provide exceptional performance, they do consume substantial amounts of power. Currently, there are various GPU power profiling mechanisms available; however, there is no standard way to assess the quality of such profiling schemes. To address this issue, in this paper, we develop an assessment methodology to rate the quality and performance of the profiling mechanism itself. Specifically, we present the assessments of four different GPU power profiling techniques: (i) Nvidia's NVML via Allinea MAP, (ii) Nvidia's NVML via direct reads, and (iii) Penguin Computing's PowerInsight (PI) via two vendor-provided drivers, and (iv) PowerInsight via Allinea MAP. In addition, we discuss the effects of moving-average filters to explain the slow variations of some of the measured power profiles. Based on our assessment, the GPU power profiling mechanism using PI device outperforms the other schemes by reliably measuring the ground-truth power profile generated by a GPU stress-test benchmark.

Authors:

^[1];

^[1]

ORNL

Publication Date:: 2018-05-01

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1474562

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) - Vancouver, , Canada - 5/21/2018 4:00:00 AM-5/25/2018 4:00:00 AM

Country of Publication:: United States

Language:: English

Citation Formats


                    Sen, Satyabrata, Imam, Neena, and Hsu, Chung-Hsing. Quality Assessment of GPU Power Profiling Mechanisms.  United States: N. p., 2018. 
        Web.  doi:10.1109/IPDPSW.2018.00113.

Copy to clipboard


                    Sen, Satyabrata, Imam, Neena, & Hsu, Chung-Hsing. Quality Assessment of GPU Power Profiling Mechanisms.  United States.  https://doi.org/10.1109/IPDPSW.2018.00113

Copy to clipboard


                    Sen, Satyabrata, Imam, Neena, and Hsu, Chung-Hsing. Tue .  
        "Quality Assessment of GPU Power Profiling Mechanisms".  United States.  https://doi.org/10.1109/IPDPSW.2018.00113.  https://www.osti.gov/servlets/purl/1474562.

Copy to clipboard


                    
@article{osti_1474562,

  title        = {Quality Assessment of GPU Power Profiling Mechanisms},

  author       = {Sen, Satyabrata and Imam, Neena and Hsu, Chung-Hsing},

  abstractNote = {Accurate component-level power measurements are nowadays essential for the design and optimization of high-performance computing (HPC) systems and applications. Particularly, as more and more heterogeneous HPC systems are developed, the characterizations of GPU power profiles have become extremely crucial because, although GPUs provide exceptional performance, they do consume substantial amounts of power. Currently, there are various GPU power profiling mechanisms available; however, there is no standard way to assess the quality of such profiling schemes. To address this issue, in this paper, we develop an assessment methodology to rate the quality and performance of the profiling mechanism itself. Specifically, we present the assessments of four different GPU power profiling techniques: (i) Nvidia's NVML via Allinea MAP, (ii) Nvidia's NVML via direct reads, and (iii) Penguin Computing's PowerInsight (PI) via two vendor-provided drivers, and (iv) PowerInsight via Allinea MAP. In addition, we discuss the effects of moving-average filters to explain the slow variations of some of the measured power profiles. Based on our assessment, the GPU power profiling mechanism using PI device outperforms the other schemes by reliably measuring the ground-truth power profile generated by a GPU stress-test benchmark.},

  doi          = {10.1109/IPDPSW.2018.00113},

  url          = {https://www.osti.gov/biblio/1474562},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2018},

  month        = {5}

}

Copy to clipboard

Conference:

View Conference (1.19 MB)

https://doi.org/10.1109/IPDPSW.2018.00113

Other availability

Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems

Conference Zou, Pengfei ; Li, Ang ; Barker, Kevin J. ; ...

Modern high-performance and warehouse computing centers show strong interest in minimizing system power consumption while satisfying customers’ quality of service (QoS). Dynamic voltage and frequency scaling (DVFS) is effective for achieving this goal. Nevertheless, automating the process online and making it transparent to users must address three major challenges: (1) Complexity — today’s hardware components (e.g., CPUs, GPUs, memory, network, etc.) can be configured in several or dozens of frequency/voltage states for satisfying divergent system demands. Given their combination and the emergence of heterogeneity, searching the optimal configuration in the design space online can be timing consuming. (2) QoS guaranteemore »« less
https://doi.org/10.1109/CCGrid49817.2020.00-37
DRAGON: breaking GPU memory capacity limits with direct NVM access

Conference Markthub, Pak ; Belviranli, Mehmet E. ; Lee, Seyong ; ...

Heterogeneous computing with accelerators is growing in importance in high performance computing (HPC). Recently, application datasets have expanded beyond the memory capacity of these accelerators, and often beyond the capacity of their hosts. Meanwhile, nonvolatile memory (NVM) storage has emerged as a pervasive component in HPC systems because NVM provides massive amounts of memory capacity at affordable cost. Currently, for accelerator applications to use NVM, they must manually orchestrate data movement across multiple memories and this approach only performs well for applications with simple access behaviors. To address this issue, we developed DRAGON, a solution that enables all classes ofmore »« less
https://doi.org/10.1109/SC.2018.00035

Full Text Available
Direct numerical simulations of turbulent reacting flows with shock waves and stiff chemistry using many-core/GPU acceleration

Journal Article Desai, Swapnil ; Kim, Yu Jeong ; Song, Wonsik ; ... - Computers and Fluids

Compressible reacting flows may display sharp spatial variation related to shocks, contact discontinuities or reactive zones embedded within relatively smooth regions. The presence of such phenomena emphasizes the relevance of shock-capturing schemes such as the weighted essentially non-oscillatory (WENO) scheme as an essential ingredient of the numerical solver. However, these schemes are complex and have more computational cost than the simple high-order compact or non-compact schemes. In this paper, we present the implementation of a seventh-order, minimally-dissipative mapped WENO (WENO7M) scheme in a newly developed direct numerical simulation (DNS) code called KAUST Adaptive Reactive Flows Solver (KARFS). In order tomore »« less
https://doi.org/10.1016/j.compfluid.2020.104787
GPU Direct I/O with HDF5

Conference Ravi, J ; Byna, S ; Koziol, Q

Exascale HPC systems are being designed with accelerators, such as GPUs, to accelerate parts of applications. In machine learning workloads as well as large-scale simulations that use GPUs as accelerators, the CPU (or host) memory is currently used as a buffer for data transfers between GPU (or device) memory and the file system. If the CPU does not need to operate on the data, then this is sub-optimal because it wastes host memory by reserving space for duplicated data. Furthermore, this “bounce buffer” approach wastes CPU cycles spent on transferring data. A new technique, NVIDIA GPUDirect Storage (GDS), can eliminatemore »« less
https://doi.org/10.1109/PDSW51947.2020.00010

Full Text Available
A performance model for GPUs with caches

Journal Article Dao, Thanh Tuan ; Kim, Jungwon ; Seo, Sangmin ; ... - IEEE Transactions on Parallel and Distributed Systems

To exploit the abundant computational power of the world's fastest supercomputers, an even workload distribution to the typically heterogeneous compute devices is necessary. While relatively accurate performance models exist for conventional CPUs, accurate performance estimation models for modern GPUs do not exist. This paper presents two accurate models for modern GPUs: a sampling-based linear model, and a model based on machine-learning (ML) techniques which improves the accuracy of the linear model and is applicable to modern GPUs with and without caches. We first construct the sampling-based linear model to predict the runtime of an arbitrary OpenCL kernel. Based on anmore »« less
Cited by 4
https://doi.org/10.1109/TPDS.2014.2333526

Full Text Available

Similar Records