Co-run scheduling with power cap on integrated cpu-gpu systems


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Scheduling Challenges and Opportunities in Integrated

In this section, rst we highlight the thermal and power e ects of scheduling for both CPU-only (e.g., SPEC work-loads [17]) and CPU/GPU applications (e.g. OpenCL ker-nels). Then, we discuss two key challenges and opportunities for e cient scheduling of kernels on CPU+GPU processors. 2.1 Thermal & Power Effects of Scheduling 1.

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Scheduling challenges and opportunities in integrated CPU+GPU

Heterogeneous processors with architecturally different devices (CPU and GPU) integrated on the same die provide good performance and energy efficiency for wide range of workloads. However, they also create challenges and opportunities in terms of scheduling workloads on the appropriate device. Current scheduling practices mainly use the characteristics of kernel workloads to

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Co-Scheduling on Fused CPU-GPU Architectures With Shared

Jul 18, 2018· Fused CPU-GPU architectures integrate a CPU and general-purpose GPU on a single die. Recent fused architectures even share the last level cache (LLC) between CPU and GPU. This enables hardware-supported byte-level coherency. Thus, CPU and GPU can execute computational kernels collaboratively, but novel methods to co-schedule work are required.

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Understanding Co-run Degradations on Integrated

Sep 15, 2014· The first study was done by Zhu et al. [36, 37] in which the authors studied co-scheduling on an integrated CPU-GPU system and considered a power cap. They devised a greedy algorithm that

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Orchestrated Co-scheduling, Resource Partitioning, and Power

May 6, 2024· The result is a system-wide optimization problem with several knobs. In particular we focus on (1) co-scheduling decisions, i.e., selecting programs to co-locate in a space

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Conflict-aware workload co-execution on SX-aurora TSUBASA

Oct 5, 2023· Another related study introduces a runtime framework for scheduling each of multiple users'' OpenCL tasks to its optimal device, either a GPU or a CPU on a CPU-GPU system (Wen and O''Boyle 2017). The runtime framework uses a performance prediction model based on machine learning at runtime to select optimal devices.

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU

May 1, 2017· We implement our power cap controller on a real multi-CPU/GPU server with low overhead, and we demonstrate that it is able to meet target power caps while maximizing the

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems

Abstract: This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered. It reveals the performance degradations caused by the co-run contentions at the levels of both memory and power. It then examines the problem of using job co-scheduling to alleviate the degradations in this less

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Orchestrated Co-Scheduling, Resource Partitioning, and Power

We comprehensively and systematically optimize (1) co-scheduling pair selections, (2) resource partitioning at both CPU and GPU, and (3) power budgeting on both CPU and GPU, using a

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Orchestrated Co-scheduling, Resource Partitioning, and

Therefore, co-scheduling, i.e., co-locating multiple processes in a space sharing manner, is a key feature to mitigate resource wastes and to maxi-mize throughput on such systems, if the

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Protecting real-time GPU kernels on integrated CPU-GPU

but they do not guarantee predictable GPU timing on integrated CPU-GPU based platforms because they do not consider the problem of the shared memory bandwidth contention between the CPU and the GPU. Integrated GPU based platforms have recently gained much attention in the real-time systems community. In [2], [12],

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[PDF] Orchestrated Co-scheduling, Resource Partitioning, and

This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered and offers several algorithms and a

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PICTure Research Group

[IPDPS''17] "Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems", Qi Zhu, Bo Wu, Xipeng Shen, Li Shen and Zhiying Wang, 31st IEEE International Parallel and Distributed Processing Symposium, Orlando, Fl, May 29--June 2, 2017.

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU

Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems. CPU-GPUシステムにおけるキャップをつCoランスケジューリング【Powered by NICT】 Publisher site

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Thermal-aware Adaptive Platform Management for Heterogeneous Embedded

Sep 22, 2021· Youngmoon Lee, Kang G. Shin, and Hoon Sung Chwa. 2019. Thermal-aware scheduling for integrated CPUs-GPU platforms. Qi Zhu, Bo Wu, Xipeng Shen, Li Shen, and Zhiying Wang. 2017. Co-run scheduling with power cap on integrated CPU-GPU systems. An Evaluation Framework for Dynamic Thermal Management Strategies in 3D MultiProcessor

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[PDF] Scheduling challenges and opportunities in integrated CPU+GPU

Oct 1, 2016· This paper provides detailed infrared imaging results that show the impact of mapping decisions on the thermal and power profiles of CPU+GPU processors and proposes techniques to characterize the OpenCL kernel workloads during run-time and map them on appropriate device under time-varying physical and CPU load conditions. Heterogeneous

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Thermal-Aware Scheduling for Integrated CPUs–GPU Platforms

As modern embedded systems like cars need high-power integrated CPUs–GPU SoCs for various real-time applications such as lane or pedestrian detection, they face greater thermal problems than be- called CPU–GPU co-scheduling, for CPUs and GPU. It deter-mines which tasks to schedule on CPUs by considering the task running on its

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[PDF] Orchestrated Co-scheduling, Resource Partitioning, and Power

This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered and offers several algorithms and a lightweight co-run performance and power predictive model for computing the performance bounds of the optimal co- schedules and finding appropriate schedules. Expand

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Protecting Real-Time GPU Applications on Integrated CPU

Abstract—Integrated CPU-GPU architecture provides excel- lent acceleration capabilities for data parallel applications on embedded platforms while meeting the size, weight and power

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Co-Cap: energy-efficient cooperative CPU-GPU frequency capping

Apr 4, 2016· Our experimental results across a large set of over 70 mobile games show that Co-Cap improves energy per frame by 10.6% and 10.0% (23.1% and 19.1% in CPU dominant applications) on average and achieves minimal frames per second (FPS) loss by 0.5% and 0.7% (1.3% and 1.7% in CPU dominant applications) on average in training- and deployment sets

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Optimizing Hardware Resource Partitioning and Job Allocations

Jan 13, 2023· Qi Zhu, Bo Wu, Xipeng Shen, Li Shen, and Zhiying Wang. 2017. Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems. In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 967–977.

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Shared Memory-contention-aware Concurrent DNN Execution for

Feb 20, 2024· Co-run scheduling with power cap on integrated cpu-gpu systems. In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 967--977.

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems

May 29, 2017· Abstract: This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered. It reveals the performance degradations caused by the co-run contentions at the levels of both memory and power. It then examines the problem of using job co-scheduling to alleviate the degradations in

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Understanding Co-run Degradations on Integrated

Sep 15, 2014· This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered and offers several algorithms and a lightweight co-run performance and power predictive model for computing the performance bounds of the optimal co- schedules and finding appropriate schedules. Expand

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems

DOI: 10.1109/IPDPS.2017.124 Corpus ID: 23878317; Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems @article{Zhu2017CoRunSW, title={Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems}, author={Qingnhua Zhu and Bo Wu and Xipeng Shen and Li Shen and Zhiying Wang}, journal={2017 IEEE International Parallel and Distributed

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU

Abstract—This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered. It reveals the performance

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[PDF] Understanding Co-Running Behaviors on Integrated CPU/GPU

Mar 1, 2017· This work ports 42 programs in Rodinia, Parboil, and Polybench benchmark suites and builds an automatic decision-tree-based model to help application developers predict the co-running performance for a given CPU-only or GPU-only program. Architecture designers tend to integrate both CPUs and GPUs on the same chip to deliver energy-efficient designs. It is still

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(PDF) Understanding Co-running Behaviors on Integrated CPU/GPU

Jan 1, 2016· Since not all the programs can benefit from integrated architectures, we build an automatic decision-tree-based model to help application developers predict the co-running performance for a given

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU Systems

This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered. It reveals the performance degradations caused by the co-run contentions at the levels of both memory and power. It then examines the problem of using job co-scheduling to alleviate the degradations in this less understood scenario.

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Optimizing Hardware Resource Partitioning and Job Allocations

Aug 29, 2022· This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered and offers several algorithms and a lightweight co-run performance and power predictive model for computing the performance bounds of the optimal co- schedules and finding appropriate schedules. Expand

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Understanding co-run performance on CPU-GPU integrated

Feb 1, 2017· This work investigates the performance implications of independently co-running CPU and GPU programs on these platforms, and produces a list of novel insights, including the important roles of operating system (OS) context switching and power management in determining the program performance. Recent years have witnessed a processor

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Understanding Co-Running Behaviors on Integrated CPU/GPU

Mar 1, 2017· The paper goes one step further by building a black-box machine prediction tool that predicts whether a program is co-run friendly or not. If the program is co-run friendly, the authors propose another analytical tool that determines the ratio of the runs on CPU versus GPU. Finally, the paper discusses an important topic: power/energy efficiency.

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Orchestrated Co-Scheduling, Resource Partitioning, and Power

Zhu et al. worked on the combination of job scheduling and power capping for integrated CPU-GPU systems [25] Zhu, Q., et al.: Co-run scheduling with power cap on integrated cpu-gpu systems. In: IPDPS. pp. 967–977 (2017) [26] Zhuravlev, S., et al.: Addressing shared resource contention in multicore processors via scheduling. In: ASPLOS. pp

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CAP: co-scheduling based on asymptotic profiling in

This subsection discusses three current scheduling strate-gies for CPU+GPU co-scheduling and Figure 2 shows the overview of these strategies. 2.2.1 Static Scheduling The traditional scheduling strategy is static scheduling. It sets the ratio of performance between the CPU and GPU statically and partitions workload according to it at the be-

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Orchestrated Co-Scheduling, Resource Partitioning, and

CPU-GPU heterogeneous systems. Q. Zhu et al. worked on the combination of job scheduling and power capping for integrated CPU-GPU systems [25], but they did not cover the following aspects: resource partitioning inside of CPU/GPU; and co-scheduling multiple processes on the GPU in a space sharing manner.

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Understanding co-run performance on CPU-GPU integrated

Apr 7, 2017· Recent years have witnessed a processor development trend that integrates central processing unit (CPU) and graphic processing unit (GPU) into a single chip. The integration helps to save some host-device data copying that a discrete GPU usually requires, but also introduces deep resource sharing and possible interference between CPU and GPU. This work

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Co-Run Scheduling with Power Cap on Integrated CPU-GPU

May 29, 2017· This paper presents the first systematic study on co-scheduling independent jobs on integrated CPU-GPU systems with power caps considered. It reveals the performance

Chat online
Orchestrated Co-Scheduling, Resource Partitioning, and

In particular we focus on (1) co-scheduling decisions, i.e., selecting programs to co-locate in a space sharing manner; (2) resource partitioning on both CPUs and GPUs; and (3) power

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About Co-run scheduling with power cap on integrated cpu-gpu systems

About Co-run scheduling with power cap on integrated cpu-gpu systems

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