Multiprocessor Support

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1 CSC 256/456: Operating Systems Multiprocessor Support John Criswell University of Rochester 1

2 Outline Multiprocessor hardware Types of multi-processor workloads Operating system issues Where to run the kernel Synchronization Where to run processes 2

3 Multiprocessor Hardware 3

4 Multiprocessor Hardware System in which two or more CPUs share full access to the main memory Each CPU might have its own cache and the coherence among multiple caches is maintained CPU CPU CPU Cache Cache Cache Memory bus Memory 4

5 Multi-core Processor Multiple processors on chip Some caches shared Some not shared CPU Cache CPU Cache Shared Cache Memory Bus 5

6 Hyper-Threading Replicate parts of processor; share other parts Create illusion that one core is two cores CPU Core Fetch Unit Decode ALU MemUnit Fetch Unit Decode 6

7 Cache Coherency Ensure processors not operating with stale memory data Writes send out cache invalidation messages CPU CPU CPU Cache Cache Cache Memory bus Memory 7

8 Non Uniform Memory Access (NUMA) Memory clustered around CPUs For a given CPU Some memory is nearby (and fast) Other memory is far away (and slow) 8

9 Multiprocessor Workloads 9

10 Multiprogramming Non-cooperating processes with no communication Examples Time-sharing systems Multi-tasking single-user operating systems make -j<very large number here> 10

11 Concurrent Servers Minimal communication between processes and threads Throughput usually the goal Examples Web servers Database servers 11

12 Parallel Programs Use parallelism to speed up computation Significant data sharing between processes and threads Examples Gaussian Elimination Matrix multiply 12

13 Operating System Issues 13

14 Three Challenges Where to run the OS How to do synchronization Where to schedule processes 14

15 Where to Run the OS? 15

16 Multiprocessor OS Bus Each CPU has its own operating system quick to port from a single-processor OS Disadvantages difficult to share things (processing cycles, memory, buffer cache) 16

17 Multiprocessor OS Master/Slave Bus All operating system functionality goes to one CPU no multiprocessor concurrency in the kernel Disadvantage OS CPU consumption may be large so the OS CPU becomes the bottleneck (especially in a machine with many CPUs) 17

18 Multiprocessor OS Shared OS A single OS instance may run on all CPUs The OS itself must handle multiprocessor synchronization multiple OS instances from multiple CPUs may access shared data structure Bus 18

19 Synchronization Issues 19

20 Synchronization Traditional atomic operations write memory Atomic compare and swap Atomic fetch and add This is very bad for multi-processors 20

21 Load-Linked and Store-Conditional Load-linked sets a bit in the cache line Cache invalidation clears bit Store-conditional checks that bit is still set in cache line Cleared bit means a cache invalidation occurred Which implies that another core wrote the memory Which implies that atomicity was violated 21

22 Performance Measures for Synchronization Latency Cost of thread management under the best case assumption of no contention for locks Throughput Rate at which threads can be created, started, and finished when there is contention 22

23 Synchronization (Fine/Coarse-Grain Locking) Fine-grain locking lock only what is necessary for critical section Coarse-grain locking locking large piece of code, much of which is unnecessary simplicity, robustness prevent simultaneous execution simultaneous execution is not possible on uniprocessor anyway 23

24 Synchronization Optimizations Avoid synchronization Per-processor data structures Lock-free data structures Use fine-grained locking to increase throughput Reuse (cache) data structures Allocation/deallocation just a few pointer operations Locks not held for very long 24

25 Where to Run Processes? 25

26 Multiprocessor Scheduling Affinity-based scheduling Try to run each process on the processor that it last ran on Takes advantage of cache locality CPU 0 CPU 1 web server parallel Gaussian elimination client/server game (civ) 26

27 Multiprocessor Scheduling Gang/Cohort scheduling Utilize all CPUs for one parallel/concurrent application at a time Cache sharing and synchronization of parallel/ concurrent applications CPU 0 CPU 1 web server parallel Gaussian elimination client/server game (civ) 27

28 Resource Management To Date Capitalistic - generation of more requests results in more resource usage Performance: resource contention can result in significantly reduced overall performance Fairness: equal time slice does not necessarily guarantee equal progress 28

29 Fairness and Security Concerns Priority inversion Poor fairness among competing applications Information leakage at chip level Denial of service attack at chip level 29

30 Disclaimer Parts of the lecture slides contain original work by Andrew S. Tanenbaum. The slides are intended for the sole purpose of instruction of operating systems at the University of Rochester. All copyrighted materials belong to their original owner(s). 30

Distributed File Systems Issues. NFS (Network File System) AFS: Namespace. The Andrew File System (AFS) Operating Systems 11/19/2012 CSC 256/456 1

Distributed File Systems Issues NFS (Network File System) Naming and transparency (location transparency versus location independence) Host:local-name Attach remote directories (mount) Single global name