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Presentation on theme: "COREY: AN OPERATING SYSTEM FOR MANY CORES"— Presentation transcript:

S. Boyd-Wickizer, H. Chen, R. Chen, Y. Mao, F. Kaashoek, R. Morris, A. Pesterev, L. Stein, M. Wu, Y. Dai, Y. Zhang, Z. Zhang MIT, Fudan U, Microsoft Research Asia, Xi’an Jiaotong U

2 Overview Paper presents a technique allowing multicore architectures to overcome memory access bottlenecks Key idea is that applications should control sharing of main memory and kernel resources Make them private by default Let each application specify which resources it want to share

3 What’s interesting It does increase system performance
Measured by microbenchmarks and application benchmarks It lets applications tell the kernel how to manage the kernel resources they use Just the opposite of what is normally done Same approach as exokernels (xOK)


5 Motivation (I) Most PCs have or will have multicore chips
Cache-coherent shared memory hardware is the new standard Performance of some OS services scales very poorly with number of cores/processors Contention for OS queues, core directory lookups Can dominate performance of some applications

6 Motivation (II) Main source of poor scalability is concurrent updates to shared data structures Consistency overhead

7 An example Microbenchmark Creates multiple threads within a process
Each thread creates a file descriptor then repeatedly duplicates it and close the result Shared resource is process file descriptor table Any modification to the table invalidates its cached copies

8 An example (II)

9 An example (III) Throughput of microbenchmark actually decreases with number of cores Problem caused by cache coherence protocol Each iteration results in a cache miss Resolving the miss requires access to a shared data structure protected by spin locks Increasing the number of threads attempting to update the table introduces queuing delays

10 Common approaches Avoiding shared data structures
Allowing concurrent access to shared data structures through Fine-grain locking Wait-free primitives Can use atomic operations or transactional memory

11 Corey approach (I) Not all instances of a given resource type must be shared Depends of application requirements Corey lets application tell kernel which instances of a particular resource type must be shared Assumes that other instances can remain private OS does not incur unnecessary sharing costs

12 Corey Organized as an exokernel Corey kernel provides Address ranges
Kernel cores Shares Most higher services are implemented as library operating systems

13 What is an exokernel? (I)
In most operating systems only privileged servers and the kernel can manage system resources The exokernel architecture delegates resource management to user applications. Applications that do not want this responsibility communicate with the exokernel through a “library OS”

14 What is an exokernel? (II)
library OS Exokernel protects but does not manage system resources User process


16 Multicore organizations
Often involve multiple chips Say four chips with four cores per chip Have a cache hierarchy on each chip L1, L2, L3 Some caches are private, other are shared Accessing a cache on a chip is much faster than accessing a cache on another chip

17 Example (I) AMD 16-core system Sixteen cores on four chips
Each core has a 64-KB L1 and a 512-KB L2 cache Each chip has a 2-MB shared L3 cache

18 X/Y where X is latency in cycles Y is bandwidth in bytes/cycle

19 Example (II) Observe that access times are non-uniform
Takes more time to access L1 or L2 cache of another core than accessing shared L3 cache Takes more time to access caches in another chip than local caches Access times and bandwidths depend on chip interconnect topology

20 Performance issues (I)
Linux spin locks Repeatedly access a shared lock variable MCS locks (Mellor-Crummey and Scott , 1991) Process requesting the lock inserts itself in a possibly empty queue Waiting processes do not interfere with each other

21 Performance issues (II)

22 Performance issues (III)
Spinlocks are better at low contention rates Require three instructions to acquire and release a lock MCS locks require fifteen instructions MCS locks are much better at higher contention rates Less synchronization overhead

23 Motivation for address ranges
Most OSes let applications chose between A single address space shared by all cores Threaded applications One private address space per core Applications forking full processes Neither of these two solutions is fully satisfactory

24 MapReduce applications
Map phase: Processes read parts of application’s inputs Generate intermediary results and store them locally Reduce phase: Processors collate results produced by multiple map instances Produce the output

25 Single address space Bad for map phase, very good for reduce phase

26 Separate address spaces
Very good for map phase, bad for reduce phase

27 Best of both worlds


29 Address ranges (I) Let applications specify which parts of their address space are shared and which are private Private address ranges will not incur any consistency overhead Shared address ranges can share their hardware page tables Minimizes soft page faults (when page is in main memory but not mapped in the process page table)

30 Address ranges (II) Application A Application B Private Private Shared

31 Address ranges (III) Kernel-provided abstraction specifying a virtual-to-physical mapping for a range of virtual addresses If multiple cores include the same address range in their address space they will share the same mapping to the same physical pages Each core can freely manipulate and delete mapping in private address ranges w/o any consequences for other core performance

32 Kernel cores In most OS, system calls are executed on the core of the invoking process Bad idea if the system call needs to access large shared data structures Kernel cores let applications dedicate cores to run specific kernel functions Avoids inter-core contention over the data these functions access

33 Symmetric multiprocessing (I)
Each core can execute both user and kernel code No execution bottleneck User or kernel code User or kernel code ...

34 Symmetric multiprocessing (II)
Works very well unless too many kernel function instances access large shared data structures Contention User or kernel code User or kernel code Shared data

35 The solution Run kernel functions that access large shared data structures No inter-core contention Kernel code User or kernel code User or kernel code Shared data

36 Shares (I) Many kernel operations involve looking up identifiers in tables to obtain a pointer to a given kernel data structure (file descriptor entry, …) Lookup tables for kernel objects that let applications specify which object identifiers are visible to other cores Each application core has a root share that is private to that core Needs no locks since its private

37 Shares (II) If two cores want to “share a share,” they create one and add the share ID to either Their private root shares or A share reachable from these root shares Allows applications to restrict sharing to kernel structures that must be shared

38 Shares (II) A's table B's table Private: no locks Private: no locks
Shared: must use locks to provide mutual exclusion

39 COREY KERNEL (not discussed)

40 SYSTEM SERVICES (not discussed)

41 Execution forking cfork(core_id) is an extension of UNIX fork() that creates a new process (pcore) on core core_id Application can specify multiple levels of sharing between parent and child Default is copy-on-write

42 Network Applications can decide to run Multiple network stacks
A single shared network stack

43 Buffer cache Shared buffer like regular UNIX buffer cache
Three modifications A lock-free tree allows multiple cores to locate cached blocks w/o contention A write scheme tries to minimize contention A scalable read/write lock


45 MapReduce applications
Modified the Phoenix MapReduce implementation to take advantage of Cory features (Metis) Each core has a separate address space with Private mappings for most data Address ranges to share the output of the map with other cores

46 Web server applications
Corey web server is built from three components Web daemons: Process HTTP requests and have their own TCP/IP stack Also referred as webd cores Kernel cores (optional) Applications



49 Current status Runs on AMD Opterons and Intel Xeons
Implementation of address ranges is tailored to architectures with hardware page tables Would provide lesser benefits on architectures where the kernel manages the page tables


51 Evaluation of address ranges (I)
Two micro benchmarks memclone: Has each core allocate its own 100 MB array and modify each page of the array mempass: Allocates a single 100MB array on one of the clones, touches each buffer page and passes it to the next core which repeats the process

52 memclone

53 mempass

54 Evaluation of address ranges (II)
Address ranges can support as well Multicore applications requiring private memory Multicore applications requiring shared memory

55 Evaluation of kernel cores (I)
Simple TCP service Accepts incoming connection requests Writes 128 bytes to the connection then closes it Two configurations “Dedicated” uses a kernel core for all network processing “Polling” uses a kernel core only to poll for packet notifications and transmit completions

56 Throughput

57 L3 cache misses

58 Evaluation of kernel cores (II)
“Dedicated” configuration Reaches network device upper bound of 110,000 connections per second with five cores Polling requires 11 cores Occasions much fewer L3 misses than “Polling” and regular Linux

59 Evaluation of shares (I)
Two microbenchmarks Each core calls share_addobj()to add a per core segment to a global share then calls share_delobj()to delete that segment Same but per core segment is added to a local share

60 Throughput

61 L3 cache misses

62 More benchmarks Both MapReduce and Webd scale up much better with Corey than with Linux

63 CONCLUSIONS In order for applications to scale up on multicore architectures, they must control sharing Since this new requirement greatly complicates the design of applications, it is best suited to application frameworks such as MapReduce


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