High-performance computing (HPC), according to its name, is traditionally oriented toward performance, especially the execution time and scalability of the computations. However, due to the high cost ...and environmental issues, energy consumption has already become a very important factor that needs to be considered. The paper presents a survey of energy-aware scheduling methods used in a modern HPC environment, starting with the problem definition, tackling various goals set up for this challenge, including a bi-objective approach, power and energy constraints, and a pure energy solution, as well as metrics related to the subject. Then, considered types of HPC systems and related energy-saving mechanisms are described, from multicore-processors/graphical processing units (GPU) to more complex solutions, such as compute clusters supporting dynamic voltage and frequency scaling (DVFS), power capping, and other functionalities. The main section presents a collection of carefully selected algorithms, classified by the programming method, e.g., machine learning or fuzzy logic. Moreover, other surveys published on this subject are summarized and commented on, and finally, an overview of the current state-of-the-art with open problems and further research areas is presented.
The Clairvoyant algorithm proposed in “A novel MPI reduction algorithm resilient to imbalances in process arrival times” was analyzed, commented and improved. The comments concern handling certain ...edge cases in the original pseudocode and description, i.e., adding another state of a process, improved cache friendliness more precise complexity estimations and some other issues improving the robustness of the algorithm implementation. The proposed improvements include skipping of idle loop rounds, simplifying generation of the ready set and management of the state array and an about 90-fold reduction in memory usage. Finally an extension enabling process arrival times (PATs) prediction was added: an additional background thread used to exchange the data with the PAT estimations. The performed tests, with a dedicated mini-benchmark executed in an HPC environment, showed correctness and improved performance of the solution, with comparison to the original or other state-of-the-art algorithms.
Two novel algorithms for the all-gather operation resilient to imbalanced process arrival patterns (PATs) are presented. The first one, Background Disseminated Ring (BDR), is based on the regular ...parallel ring algorithm often supplied in MPI implementations and exploits an auxiliary background thread for early data exchange from faster processes to accelerate the performed all-gather operation. The other algorithm, Background Sorted Linear synchronized tree with Broadcast (BSLB), is built upon the already existing PAP-aware gather algorithm, that is, Background Sorted Linear Synchronized tree (BSLS), followed by a regular broadcast distributing gathered data to all participating processes. The background of the imbalanced PAP subject is described, along with the PAP monitoring and evaluation topics. An experimental evaluation of the algorithms based on a proposed mini-benchmark is presented. The mini-benchmark was performed over 2,000 times in a typical HPC cluster architecture with homogeneous compute nodes. The obtained results are analyzed according to different PATs, data sizes, and process numbers, showing that the proposed optimization works well for various configurations, is scalable, and can significantly reduce the all-gather elapsed times, in our case, up to factor 1.9 or 47% in comparison with the best state-of-the-art solution.
Imbalanced process arrival patterns (PAPs) are ubiquitous in many parallel and distributed systems, especially in HPC ones. The collective operations, e.g. in MPI, are designed for equal process ...arrival times, and are not optimized for deviations in their appearance. We propose eight new PAP-aware algorithms for the scatter and gather operations. They are binomial or linear tree adaptations introducing additional process ordering and (in some cases) additional activities in a special background thread. The solution was implemented using one of the most popular open source MPI compliant library (OpenMPI), and evaluated in a typical HPC environment using a specially developed benchmark as well as a real application: FFT. The experimental results show a significant advantage of the proposed approach over the default OpenMPI implementation, showing good scalability and high performance with the FFT acceleration for the communication run time: 16.7% and for the total application execution time: 3.3%.
Two new algorithms for the all-reduce operation optimized for imbalanced process arrival patterns (PAPs) are presented: (1) sorted linear tree, (2) pre-reduced ring as well as a new way of online PAP ...detection, including process arrival time estimations, and their distribution between cooperating processes was introduced. The idea, pseudo-code, implementation details, benchmark for performance evaluation and a real case example for machine learning are provided. The results of the experiments were described and analyzed, showing that the proposed solution has high scalability and improved performance in comparison with the usually used ring and Rabenseifner algorithms.
Modeling energy consumption of parallel applications Paweł Czarnul; Jarosław Kuchta; Paweł Rościszewski ...
Annals of Computer Science and Information Systems,
11/2016, Letnik:
8
Conference Proceeding, Journal Article
The recently deployed supercomputer Tryton, located in the Academic Computer Center of Gdansk University of Technology, provides great means for massive parallel processing. Moreover, the status of ...the Center as one of the main network nodes in the PIONIER network enables the fast and reliable transfer of data produced by miscellaneous devices scattered in the area of the whole country. The typical examples of such data are streams containing radio-telescope and satellite observations. Their analysis, especially with real-time constraints, can be challenging and requires the usage of dedicated software components. We propose a solution for such parallel analysis using the supercomputer, supervised by the KASKADA platform, which with the conjunction with immerse 3D visualization techniques can be used to solve problems such as pulsar detection and chronometric or oil-spill simulation on the sea surface.
GPU accelerators have become essential to the recent advance in computational power of high-performance computing (HPC) systems. Current HPC systems’ reaching an approximately 20–30 mega-watt power ...demand has resulted in increasing CO2 emissions, energy costs and necessitate increasingly complex cooling systems. This is a very real challenge. To address this, new mechanisms of software power control could be employed. In this paper, a dynamic new method of limiting software power is introduced on one of the latest NVIDIA GPUs: a software tool called the Dynamic Energy-Performance Optimiser (DEPO). DEPO minimizes the energy consumption of the CUDA based GPU workloads, with respect to one of the three given metrics: minimum of energy (E), Energy-Delay product (EDP) and Energy-Delay sum (EDS).
The tool gathers power measurements from NVIDIA Management Library (NVML). Measuring the application progress at runtime is based on CUDA Profiling Tools Interface (CUPTI) kernel-counting. We have evaluated the DEPO tool on the NVIDIA RTX A4500 and A100 GPUs with machine learning workloads. Depending on the application (training of neural networks: Resnet152, Densenet161, VGG-19 or a GEMM benchmark) for the E target metric, we were able to obtain energy savings exceeding 22% for both NVIDIA A100 and RTX A4500 GPUs while the performance drop has never been higher than 20%. Using one of the bi-objective EDP or EDS metrics allowed finding configurations resulting in 15% or 18% of energy saved with only 8% of performance loss. For most of the experiments the percentage-wise performance penalty is lower than the energy savings. This demonstrates its potential for energy consumption reduction in HPC systems with GPU accelerators.
•Dynamic GPU power capping with on-line performance tracing.•Low overhead software tool for energy efficient GPU computing.•Energy consumption optimization for typical Deep CNN GPU workloads.•Benchmarking the energy efficient CNN training for latest NVIDIA Ampere GPUs.
The paper presents state of the art of energy-aware high-performance computing (HPC), in particular identification and classification of approaches by system and device types, optimization metrics, ...and energy/power control methods. System types include single device, clusters, grids, and clouds while considered device types include CPUs, GPUs, multiprocessor, and hybrid systems. Optimization goals include various combinations of metrics such as execution time, energy consumption, and temperature with consideration of imposed power limits. Control methods include scheduling, DVFS/DFS/DCT, power capping with programmatic APIs such as Intel RAPL, NVIDIA NVML, as well as application optimizations, and hybrid methods. We discuss tools and APIs for energy/power management as well as tools and environments for prediction and/or simulation of energy/power consumption in modern HPC systems. Finally, programming examples, i.e., applications and benchmarks used in particular works are discussed. Based on our review, we identified a set of open areas and important up-to-date problems concerning methods and tools for modern HPC systems allowing energy-aware processing.
This paper provides a review of contemporary methodologies and APIs for parallel programming, with representative technologies selected in terms of target system type (shared memory, distributed, and ...hybrid), communication patterns (one-sided and two-sided), and programming abstraction level. We analyze representatives in terms of many aspects including programming model, languages, supported platforms, license, optimization goals, ease of programming, debugging, deployment, portability, level of parallelism, constructs enabling parallelism and synchronization, features introduced in recent versions indicating trends, support for hybridity in parallel execution, and disadvantages. Such detailed analysis has led us to the identification of trends in high-performance computing and of the challenges to be addressed in the near future. It can help to shape future versions of programming standards, select technologies best matching programmers’ needs, and avoid potential difficulties while using high-performance computing systems.
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