3parallel memoryallocation

Parallel Memory AllocationParallel Memory Allocation
Steven Saunders

Steven Saunders 2 Parallel Memory Allocation
Introduction
 Fallacy: All dynamic memory allocators are
either scalable, effective or efficient...
 Truth: no allocator exists that handles all
situations, allocation patterns and memory
hierarchies best (serial or parallel)
 Research results are difficult to compare
– no standard benchmarks, simulators vs. real tests
 Distributed memory, garbage collection, locality
not considered in this talk

Definitions
 Heap
– pool of memory available for allocation or
deallocation of arbitrarily-sized blocks in arbitrary
order that will live an arbitrary amount of time
 Dynamic Memory Allocator
– used to request or return memory blocks to the heap
– aware only of the size of a memory block, not its
type and value
– tracks which parts of the heap are in use and which
parts are available for allocation

Design of an Allocator
 Strategy - consider regularities in program
behavior and memory requests to determine a
set of acceptable policies
 Policy - decide where to allocate a memory
block within the heap
 Mechanism - implement the policy using a set
of data structures and algorithms
Emphasis has been on policies and mechanisms!

Strategy
 Ideal Serial Strategy
– “put memory blocks where they won’t cause
fragmentation later”
– Serial program behavior:
 ramps, peaks, plateaus
 Parallel Strategies
– “minimize unrelated objects on the same page”
– “bound memory blowup and minimize false sharing”
– Parallel program behavior:
 SPMD, producer-consumer

Policy
 Common Serial Policies:
– best fit, first fit, worst fit, etc.
 Common Techniques:
– splitting - break large blocks into smaller pieces to
satisfy smaller requests
– coalescing - free blocks to satisfy bigger requests
 immediate - upon deallocation
 deferred - wait until requests cannot be satisfied

Mechanism
 Each block contains header information
– size, in use flag, pointer to next free block, etc.
 Free List - list of memory blocks available for
allocation
– Singly/Doubly Linked List - each free block points to
the next free block
– Boundary Tag - size info at both ends of block
 positive indicates free, negative indicates in use
– Quick Lists - multiple linked lists, where each list
contains blocks of equal size

Performance
 Speed - comparable to serial allocators
 Scalability - scale linearly with processors
 False Sharing - avoid actively causing it
 Fragmentation - keep it to a minimum

0 1
Memory
Cache
Performance (False Sharing)
 Multiple processors inadvertently share data
that happens to reside in the same cache line
– padding solves problem, but greatly increases
fragmentation
0 1
0 1 0 1
X X X X

Performance (Fragmentation)
 Inability to use available memory
 External - available blocks cannot satisfy
requests (e.g., too small)
 Internal - block used to satisfy a request is
larger than the requested size

Performance (Blowup)
 Out of control fragmentation
– unique to parallel allocators
– allocator correctly reclaims freed memory but fails to
reuse it for future requests
 available memory not seen by allocating processor
– p threads that serialize one after
another require O(s) memory
– p threads that execute in parallel
require O(ps) memory
– p threads that execute interleaved on
1 processor require O(ps) memory...
...
x = malloc(s);
free(x);
...
Example:
blowup from exchanging memory (Producer-Consumer)

Parallel Allocator Taxonomy
 Serial Single Heap
– global lock protects the heap
 Concurrent Single Heap
– multiple free lists or a concurrent free list
 Multiple Heaps
– processors allocate from any of several heaps
 Private Heaps
– processors allocate exclusively from a local heap
 Global & Local Heaps
– processors allocate from a local and a global heap

Serial Single Heap
 Make an existing serial allocator thread-safe
– utilize a single global lock for every request
 Performance
– high speed, assuming a fast lock
– scalability limited
– false sharing not considered
– fragmentation bounded by serial policy
 Typically production allocators
– IRIX, Solaris, Windows

Concurrent Single Heap
 Apply concurrency to existing serial allocators
– use quick list mechanism, with a lock per list
 Performance
– moderate speed, could require many locks
– scalability limited by number of requested sizes
– false sharing not considered
– fragmentation bounded by serial policy
 Typically research allocators
– Buddy System, MFLF (multiple free list first),
NUMAmalloc

Concurrent Single (Buddy System)
 Policy/Mechanism
– one free list per memory block size: 1,2,4,…,2i
– blocks recursively split on list i into 2 buddies for list
i-1 in order to satisfy smaller requests
– only buddies are coalesced to satisfy larger requests
– each free list can be individually locked
– trade speed for reduced fragmentation
 if free list empty, a thread’s malloc enters a wait queue
 malloc could be satisfied by another thread freeing memory
or by breaking a higher list’s block into buddies (whichever
finishes first)
 reducing the number of splits reduces fragmentation by
leaving more large blocks for future requests

Concurrent Single (Buddy System)
 Performance
– moderate speed, complicated locking/queueing,
although buddy split/coalesce code is fast
– false sharing very likely
– high internal fragmentation
1
2
4
8
16
Thread 1 Thread 2
x = malloc(8);
1
x = malloc(5);
2
y = malloc(8); free(x);
1
2
4
8
16
1

Concurrent Single (MFLF)
 Policy/Mechanism
– set of quick lists to satisfy small requests exactly
 malloc takes first block in appropriate list
 free returns block to head of appropriate list
– set of misc lists to satisfy large requests quickly
 each list labeled with range of block sizes, low…high
 malloc takes first block in list where request < low
– trades linear search for internal fragmentation
 free returns blocks to list where low < request < high
– each list can be individually locked and searched

Concurrent Single (MFLF)
 Performance
– high speed
– false sharing very likely
 Approach similar to current state-of-the-art
serial allocators

Concurrent Single (NUMAmalloc)
 Strategy - minimize co-location of unrelated
objects on the same page
– avoid page level false sharing (DSM/software DSM)
 Policy - place same-sized requests in the same
page (heuristic hypothesis)
– basically MFLF on the page level
 Performance
– high speed
– false sharing: helps page level but not cache level

Multiple Heaps
 List of multiple heaps
– individually growable, shrinkable and lockable
– threads scan list looking for first available (trylock)
– threads may cache result to reduce next lookup
 Performance
– moderate speed, limited by # list scans and lock
– scalability limited by number of heaps and traffic
– false sharing unintentionally reduced
– blowup increased (up to O(p))
 Typically production allocators
– ptmalloc (Linux), HP-UX

Private Heaps
 Processors exclusively utilize a local private
heap for all allocation and deallocation
– eliminates need for locks
 Performance
– extremely high speed
– scalability unbounded
– reduced false sharing
 pass memory to another thread
– blowup unbounded
 Both research and production allocators
– CILK, STL

Global & Local Heaps
 Processors generally utilize a local heap
– reduces most lock contention
– private memory is acquired/returned to global heap
(which is always locked) as necessary
 Performance
– high speed, less lock contention
– scalability limited by number of locks
– low false sharing
– blowup bounded (O(1))
 Typically research allocators
– VH, Hoard

Global & Local Heaps (VH)
 Strategy - exchange memory overhead for
improved scalability
 Policy
– memory broken into stacks of size m/2
– global heap maintains a LIFO of stack pointers that
local heaps can use
 global push releases a local heap’s stack
 global pop acquires a local heap’s stack
– local heaps maintain an Active and Backup stack
 local operations private, i.e. don’t require locks
 Mechanism - serial free list within stacks

Global & Local Heaps (VH)
 Memory Usage = M + m*p
– M = amount of memory in use by program
– p = number of processors
– m = private memory (2 size m/2 stacks)
 higher m reduces number of global heap lock operations
 lower m reduces memory usage overhead
B
0
A B
1
A B
p
A
global heap
private heap

Global & Local Heaps (Hoard)
 Strategy - bound blowup and min. false sharing
 Policy
– memory broken into superblocks of size S
 all blocks within a superblock are of equal size
– global heap maintains a set of available superblocks
– local heaps maintain local superblocks
 malloc satisfied by local superblock
 free returns memory to original allocating superblock (lock!)
 superblocks acquired as necessary from global heap
 if local usage drops below a threshold, superblocks are
returned to the global heap
 Mechanism – private superblock quick lists

Global & Local Heaps (Hoard)
 Memory Usage = O(M + p)
– M = amount of memory in use by program
– p = number of processors
 False Sharing
– since malloc is satisfied by a local superblock, and
free returns memory to the original superblock,
false sharing is greatly reduced
– worst case: a non-empty superblock is released to
global heap and another thread acquires it
 set superblock size and emptiness threshold to minimize

Global & Local Heaps (Compare)
 VH proves a tighter memory bound than Hoard
and only requires global locks
 Hoard has a more flexible local mechanism and
considers false sharing
 They’ve never been compared!
– Hoard is production quality and would likely win

Summary
 Memory allocation is still an open problem
– strategies addressing program behavior still
uncommon
 Performance Tradeoffs
– speed, scalability, false sharing, fragmentation
 Current Taxonomy
– serial single heap, concurrent single heap, multiple
heaps, private heaps, global & local heaps

References
Serial Allocation
1) Paul Wilson, Mark Johnstone, Michael Neely, David Boles.
Dynamic Storage Allocation: A Survey and Critical Review. 1995 International Workshop on
Memory Management.
Shared Memory Multiprocessor Allocation
Concurrent Single Heap
2) Arun Iyengar. Scalability of Dynamic Storage Allocation Algorithms. Sixth Symposium on the
Frontiers of Massively Parallel Computing. October 1996.
3) Theodore Johnson, Tim Davis. Space Efficient Parallel Buddy Memory Management. The Fourth
International Conference on Computing and Information (ICCI'92). May 1992.
4) Jong Woo Lee, Yookun Cho. An Effective Shared Memory Allocator
for Reducing False Sharing in NUMA Multiprocessors. IEEE Second International Conference
on Algorithms & Architectures for Parallel Processing (ICAPP'96).
Multiple Heaps
5) Wolfram Gloger. Dynamic Memory Allocator Implementations in Linux System Libraries.
Global & Local Heaps
6) Emery Berger, Kathryn McKinley, Robert Blumofe, Paul Wilson. Hoard: A Scalable Memory
Allocator for Multithreaded Applications. The Ninth International Conference on Architectural
Support for Programming Languages and Operating Systems (ASPLOS-IX). November 2000.
7) Voon-Yee Vee, Wen-Jing Hsu. A Scalable and Efficient Storage Allocator
on Shared-Memory Multiprocessors. The International Symposium on Parallel Architectures,
Algorithms, and Networks (I-SPAN'99). June 1999.

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