Progress Report 2013/11/07. Outline Further studies about heterogeneous multiprocessing other than ARM Cache miss issue Discussion on task scheduling.

Slides:



Advertisements
Similar presentations
Washington WASHINGTON UNIVERSITY IN ST LOUIS Real-Time: Periodic Tasks Fred Kuhns Applied Research Laboratory Computer Science Washington University.
Advertisements

Energy-efficient Task Scheduling in Heterogeneous Environment 2013/10/25.
4. Workload directed adaptive SMP multicores
Scheduling in Distributed Systems Gurmeet Singh CS 599 Lecture.
1 “Scheduling with Dynamic Voltage/Speed Adjustment Using Slack Reclamation In Multi-processor Real-Time Systems” Dakai Zhu, Rami Melhem, and Bruce Childers.
CS 149: Operating Systems February 3 Class Meeting
Dynamic Thread Assignment on Heterogeneous Multiprocessor Architectures Pree Thiengburanathum Advanced computer architecture Oct 24,
Resource Management §A resource can be a logical, such as a shared file, or physical, such as a CPU (a node of the distributed system). One of the functions.
CPE555A: Real-Time Embedded Systems
Energy-Efficient System Virtualization for Mobile and Embedded Systems Final Review 2014/01/21.
Towards Power Efficiency on Task-Based, Decoupled Access-Execute Models Konstantinos Koukos David Black-Schaffer Vasileios Spiliopoulos Stefanos Kaxiras.
Higher Computing: Unit 1: Topic 3 – Computer Performance St Andrew’s High School, Computing Department Higher Computing Topic 3 Computer Performance.
Microarchitectural Approaches to Exceeding the Complexity Barrier © Eric Rotenberg 1 Microarchitectural Approaches to Exceeding the Complexity Barrier.
Soft Real-Time Semi-Partitioned Scheduling with Restricted Migrations on Uniform Heterogeneous Multiprocessors Kecheng Yang James H. Anderson Dept. of.
Project Overview 2014/05/05 1. Current Project “Research on Embedded Hypervisor Scheduler Techniques” ◦ Design an energy-efficient scheduling mechanism.
Towards Feasibility Region Calculus: An End-to-end Schedulability Analysis of Real- Time Multistage Execution William Hawkins and Tarek Abdelzaher Presented.
Aleksandra Tešanović Low Power/Energy Scheduling for Real-Time Systems Aleksandra Tešanović Real-Time Systems Laboratory Department of Computer and Information.
Multiprocessing Memory Management
Review: Operating System Manages all system resources ALU Memory I/O Files Objectives: Security Efficiency Convenience.
Copyright ©2009 Opher Etzion Event Processing Course Engineering and implementation considerations (related to chapter 10)
EE 249, Fall Discussion: Scheduling Haibo Zeng Amit Mahajan.
By Group: Ghassan Abdo Rayyashi Anas to’meh Supervised by Dr. Lo’ai Tawalbeh.
Multi-core processors. History In the early 1970’s the first Microprocessor was developed by Intel. It was a 4 bit machine that was named the 4004 The.
Low Power Processor --- For Mobile Devices Ziwei Zheng Wenjia Ouyang.
Microprocessors SUBTITLE Team 3: David Meadows David Foster Sichao Ni Khareem Gordon.
Authors: Tong Li, Dan Baumberger, David A. Koufaty, and Scott Hahn [Systems Technology Lab, Intel Corporation] Source: 2007 ACM/IEEE conference on Supercomputing.
1 Previous lecture review n Out of basic scheduling techniques none is a clear winner: u FCFS - simple but unfair u RR - more overhead than FCFS may not.
1 Single-ISA Heterogeneous Multi-Core Architectures: The Potential for Processor Power Reduction Rakesh Kumar, Keith I. Farkas, Norman P. Jouppi, Parthasarathy.
A S CHEDULABILITY A NALYSIS FOR W EAKLY H ARD R EAL - T IME T ASKS IN P ARTITIONING S CHEDULING ON M ULTIPROCESSOR S YSTEMS Energy Reduction in Weakly.
Thread Criticality Predictors for Dynamic Performance, Power, and Resource Management in Chip Multiprocessors Abhishek Bhattacharjee and Margaret Martonosi.
Multiprocessor Real-time Scheduling Jing Ma 马靖. Classification Partitioned Scheduling In the partitioned approach, the tasks are statically partitioned.
Progress Report 2014/02/12. Previous in IPDPS’14 Energy-efficient task scheduling on per- core DVFS architecture ◦ Batch mode  Tasks with arrival time.
An Energy-Efficient Hypervisor Scheduler for Asymmetric Multi- core 1 Ching-Chi Lin Institute of Information Science, Academia Sinica Department of Computer.
Real-Time Scheduling CS 3204 – Operating Systems Lecture 20 3/3/2006 Shahrooz Feizabadi.
Prepare by : Ihab shahtout.  Overview  To give an overview of fixed priority schedule  Scheduling and Fixed Priority Scheduling.
Hard Real-Time Scheduling for Low- Energy Using Stochastic Data and DVS Processors Flavius Gruian Department of Computer Science, Lund University Box 118.
5 May CmpE 516 Fault Tolerant Scheduling in Multiprocessor Systems Betül Demiröz.
An Energy-efficient Task Scheduler for Multi-core Platforms with per-core DVFS Based on Task Characteristics Ching-Chi Lin Institute of Information Science,
CSE 522 Real-Time Scheduling (2)
Floating Point Numbers & Parallel Computing. Outline Fixed-point Numbers Floating Point Numbers Superscalar Processors Multithreading Homogeneous Multiprocessing.
IIS Progress Report 2015/10/12. Problem Revisit Given a set of virtual machines, each contains some virtual cores with resource requirements. Decides.
Real-Time Scheduling CS 3204 – Operating Systems Lecture 13 10/3/2006 Shahrooz Feizabadi.
1 of 14 1/34 Embedded Systems Design: Optimization Challenges Paul Pop Embedded Systems Lab (ESLAB) Linköping University, Sweden.
Outline Models Design of experiments Current Scheduler Completely Fair Scheduler(CFS) ◦ Since Linux ◦ /kernel/sched.c ◦ Maintain balance (fairness)
Research on Embedded Hypervisor Scheduler Techniques 2014/10/02 1.
Progress Report 2013/08/22. Model Modification Each core works under the same frequency due to hardware limitation. A task can have different processing.
Shouqing Hao Institute of Computing Technology, Chinese Academy of Sciences Processes Scheduling on Heterogeneous Multi-core Architecture.
Migration Cost Aware Task Scheduling Milestone Shraddha Joshi, Brian Osbun 10/24/2013.
Spark on Entropy : A Reliable & Efficient Scheduler for Low-latency Parallel Jobs in Heterogeneous Cloud Huankai Chen PhD Student at University of Kent.
“Temperature-Aware Task Scheduling for Multicore Processors” Masters Thesis Proposal by Myname 1 This slides presents title of the proposed project State.
Distributed Process Scheduling- Real Time Scheduling Csc8320(Fall 2013)
IIS Progress Report 2016/01/11. Goal Propose an energy-efficient scheduler that minimize the power consumption while providing sufficient computing resources.
Temperature and Power Management
Progress Report 2014/05/23.
Microarchitecture.
Wayne Wolf Dept. of EE Princeton University
Multi-core processors
Ching-Chi Lin Institute of Information Science, Academia Sinica
Multi-core processors
Some challenges in heterogeneous multi-core systems
CS 143A - Principles of Operating Systems
Computer Architecture Lecture 4 17th May, 2006
Evaluating Asymmetric Multiprocessing for Mobile Applications
Outline Scheduling algorithms Multi-processor scheduling
Progress Report 2014/04/23.
Processes and operating systems
Research on Embedded Hypervisor Scheduler Techniques
Real-Time Process Scheduling Concepts, Design and Implementations
Real-Time Process Scheduling Concepts, Design and Implementations
Presentation transcript:

Progress Report 2013/11/07

Outline Further studies about heterogeneous multiprocessing other than ARM Cache miss issue Discussion on task scheduling

Manufacturers Other than ARM Qualcomm ◦ aSMP(Asynchronous Symmetrical Multi- Processing) ◦ Krait:  Per-core DCVS (Dynamic Clock and Voltage Scaling).  Core that is not being used can be completely collapsed independently.  Reduce the need for hypervisors or more complex software management of disparate cores.

Manufacturers Other than ARM Nvidia ◦ vSMP(Variable Symmetric Multiprocessing) ◦ Tegra 3  4 high performance Cortex A9 main processor + 1 energy-efficient Cortex A9 Companion processor.  Cannot active companion processor and main processor simultaneously.  Main processors have to use the same frequency.

HSA Foundation

Cache Miss Issue “For each switching between big(A15) and A7(LITTLE), the L2 cache is cleaned, thus cause memory access overhead.”

Cache Miss Issue Unless a chip(All A15 or All A7) is shutdown, clean L2 cache for each switching between A15 and A7 is weird. A15 L1 A15 L1 A15 L1 A15 L1 A7 L1 A7 L1 A7 L1 A7 L1 L2

Task Scheduling Take loading of each task into consideration. For a given task, assume it behavior: ◦ Computation Ops: n time units. ◦ Memory Ops: 1 time unit. Different core frequencies cause different loadings. ◦ F = 1, loading = n/(n+1) ◦ F= 2, loading = n/(n+2) ◦ F= 4, loading = n/(n+4)

Single Core For a given set of tasks and their behaviors, find the minimum frequency such the loading of the core is 100%. ◦ Lower frequency: loading = 100%, but the performance decrease. ◦ Higher frequency: loading < 100%, consume more (dynamic) power.

Scheduling on HMP According to the core capability, assign processes in the runqueue to core. Each core apply DVFS/DCVS individually. However, this does not apply for big.LITTLE. ◦ Each (pair of) core is homogeneous.

Big.LITTLE core Scheduling Assume that we have n pairs of big.LITTLE cores. ◦ Initially all pairs use LITTLE core. Assume we know the following information of a task T k. ◦ Task deadline. ◦ Estimated execution time on big core. ◦ Estimated execution time on LITTLE core.

Heuristic Mentioned Last Time First, we define “urgency” U to indicate the priority of a task. For Task T k ◦ 0 < U k ≦ 1, then task T k can be finished before deadline on LITTLE core ◦ U k > 1, then task T k can’t be finished before deadline on LITTLE core.

Core Switching Switch one LITTLE core to big core if there exists a task T k with urgency U k > 1. Find all the Tasks {T j, with U j > 0.8}, assign these tasks to big cores. Switch big cores to LITTLE cores if there is no task with urgency greater than 0.8.

Discussion