Evaluating Orthogonality between Application Auto tuning and Run Time Resource Management for Adaptive OpenCL Applications
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1 Evaluating Orthogonality between Application Auto tuning and Run Time Resource Management for Adaptive OpenCL Applications Edoardo Paone, Davide Gadioli, Gianluca Palermo, Vittorio Zaccaria, Cristina Silvano Politecnico di Milano
2 Computer Architecture Evolution um um Pentium um Core2 Duo 65nm Nehalem 45nm Time The number of transistors incorporated in a chip will approximately double every two years Gordon Moore, Intel co-founder 2
3 Moore s Law on Performance Performance um um Pentium 4 0.8um Core2 Duo 65nm Nehalem 45nm
4 Moore s Law on Performance Performance The Golden Era: - Single-processor - 1 st Power Wall um um Pentium 4 0.8um Core2 Duo 65nm Nehalem 45nm
5 Moore s Law on Performance Performance The Multicore Era: - 2 to 16 cores - On-chip shared LL$ um um Pentium 4 0.8um Core2 Duo 65nm Nehalem 45nm - Programmability challenge
6 Moore s Law on Performance Performance? The Manycore Era: - Larger # of cores - Networks on-chip um um Pentium 4 0.8um Core2 Duo 65nm Nehalem 45nm Programmability challenge + Dynamic Resource Management 3
7 Main Idea In the context of resource consolidation, analyze the orthogonal effects of: Resource Management Application Auto Tuning Approximate computing Target Platforms 4
8 Main Idea In the context of resource consolidation, analyze the orthogonal effects of: Resource Management Application Auto Tuning Approximate computing Target Platforms 4 Multicore Platform
9 Run Time Resource Management App1 App2 App3 RTRM Target Platform Amit Kumar Singh, Muhammad Shafique, Akash Kumar, and Jörg Henkel. Mapping on multi/many core systems: survey of current and emerging trends. In Proceedings of the 50th Annual Design Automation Conference (DAC)
10 RTRM Overview App1 App2 App3 Accounting Mapping RTRM Target Platform 6
11 RTRM Overview Resource accounting phase grants resources to critical workloads while optimize resource usage by best effort workloads Accounting Mapping App1 App2 App RTRM Target Platform 6
12 RTRM Overview Resource accounting phase grants resources to critical workloads while optimize resource usage by best effort workloads Mapping phase maps virtual resources on physical resources to achieve optimal platform usage to handle run time variations Accounting Mapping App1 App2 App RTRM Target Platform 6
13 Application Auto Tuning Key idea is that most of the applications are configurable thanks to a set of parameters 7
14 Application Auto Tuning Key idea is that most of the applications are configurable thanks to a set of parameters 7
15 Application Auto Tuning Key idea is that most of the applications are configurable thanks to a set of parameters Parameters: Color 7
16 Application Auto Tuning Key idea is that most of the applications are configurable thanks to a set of parameters Parameters: Color Shape 7
17 Application Auto Tuning Key idea is that most of the applications are configurable thanks to a set of parameters Parameters: Color Shape Size 7
18 Application Auto Tuning Key idea is that most of the applications are configurable thanks to a set of parameters Run-Time Knobs 7
19 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance QoR Performance 8
20 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance Autonomous Video-surveillance System 8
21 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance Video Frame Rate Video Resolution Autonomous Video-surveillance System 8
22 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance Video Frame Rate Video Resolution Autonomous Video-surveillance System 8
23 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance Video Frame Rate Video Resolution Autonomous Video-surveillance System 8
24 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance Video Frame Rate Video Resolution Autonomous Video-surveillance System 8
25 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance Video Frame Rate Video Resolution Autonomous Video-surveillance System 8
26 Why Run Time Knobs & Auto Tuning? In some applications internal knobs that can be used to trade off between application quality of results and performance Video Frame Rate Video Resolution Autonomous Video-surveillance System 8
27 9 Application Auto Tuning Framework
28 Application Auto Tuning Framework Execution Loop 9
29 Application Auto Tuning Framework Monitoring 9
30 Application Auto Tuning Framework Re-Configure 9
31 Orthogonality Concept Requests Resources Platform OS Target HW Platform 10
32 Orthogonality Concept Requests Resources Exploitation of OpenCL Device Fission to limit resource requests Platform OS Target HW Platform 10
33 Orthogonality Concept Requests Resources Run-Time Resource Manager Exploitation of OpenCL Device Fission to limit resource requests Platform OS Target HW Platform 10
34 The Multi View Case Study 2 eyes = 3 dimensions 11
35 Implementation 1 P R P L Q R Q L Q L Q P L P D P CAM LEFT D Q Q R P R CAM LEFT CAM RIGHT CAM RIGHT 1 Ke Zhang, Jiangbo Lu, and Gauthier Lafruit, Cross-Based Local Stereo Matching Using Orthogonal Integral Images, IEEE Transactions On Circuits and Systems For Video Technology, Vol. 19, No. 7, July
36 Pixel disparity 36 Left camera Right camera reference disparity 13
37 Pixel disparity 1 Left camera Right camera 2 5 Application Knobs 3 reference disparity QoR Disparity Error 13
38 Experimental Setup Target Platform AMD NUMA Architecture: 4 nodes 4 cores OpenCL 1.2 run time provided by AMD 14
39 Experimental Setup Target Platform AMD NUMA Architecture: 4 nodes 4 cores OpenCL 1.2 run time provided by AMD Workload Definition: Single application multiple instances Dynamic workload in terms of start time, amount of data to process, frame rate goal 14
40 Experimental Setup Target Platform AMD NUMA Architecture: 4 nodes 4 cores OpenCL 1.2 run time provided by AMD Workload Definition: Single application multiple instances Dynamic workload in terms of start time, amount of data to process, frame rate goal Evaluation Metrics Normalized Actual Penalty (Performance/Quality metric) User satisfaction in terms of Application Frame Rate Normalized Application Error (Quality metric) User satisfaction in terms of quality of the resulting image (1/QoR) Difference w.r.t. off line profiling (Predictability metric) 14
41 Application Auto Tuning Effects 15
42 15 Application Auto Tuning Effects
43 15 Application Auto Tuning Effects
44 Comparative Analysis Application Auto-tuning OFF ON Run-Time Resource Management OFF ON PLAIN-LINUX (No Device Fission) PLAIN-RTRM ADAPTIVE-LINUX ADAPTIVE-RTRM 16
45 Run Time Results Missed Deadlines Design-Time Vs Run-Time Profiling QoR Disparity Error #Multi-View Apps
46 Run Time Results Missed Deadlines Design-Time Vs Run-Time Profiling QoR Disparity Error #Multi-View Apps
47 Run Time Results Missed Deadlines Design-Time Vs Run-Time Profiling QoR Disparity Error #Multi-View Apps
48 Run Time Results Missed Deadlines Design-Time Vs Run-Time Profiling QoR Disparity Error #Multi-View Apps
49 Run Time Results Missed Deadlines Design-Time Vs Run-Time Profiling QoR Disparity Error #Multi-View Apps
50 Run Time Results APPS 18
51 Resource Aware AS RTM Requests Resources Resource Availability Platform OS Target HW Platform 19
52 Resource Aware AS RTM Requests Resources Resource Availability Platform OS Target HW Platform 19
53 Conclusions We considered the problem of managing multiple OpenCL applications for server consolidation on multicore platforms We implemented an approach exploiting run time management frameworks operating both at application level or at OS/resource level Analysis of results: Auto tuning is necessary to modulate performance and QoR Resource awareness is needed for predictability by means of resource isolation (RTRM) or simple monitor (RA AS RTM) 20
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