Multitasking and Parallelism
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Transcript of Multitasking and Parallelism
Kristopher WindsorCS 147, Fall 2008
Parallel processing on one core Multicore usage, difficulties, and next
steps Alternatives to multicore CPUs Multicore benchmarks
Single data stream Multiple data streams
Single instruction stream
SISD (Pentium 4) SIMD (x86 MMX)
Multiple instruction streams
MISD (not used) MIMD (Xeon / Clovertown)
Multiple instructions and / or data can be processed each cycle, for batch-processing efficiency
For example, MMX has many ALUs operate simultaneously to process multiple data
Vector architecture is similar to SIMD, but its speed comes from parallel data movement, not parallel data processing
Required whenever there are more threads than cores
There are multiple ways for a core to switch to a different thread Fine-grained multithreading: switch every
cycle Course-grained multithreading: switch when
the current thread is stalled (IE it is waiting for some data to come back from the RAM)
Simultaneous multithreading (SMT): multiple threads are processed each cycle
Clock speed limits for each core due to heat Heat produced is exponentially related to
clock speed, and cooling methods are limited This limit has already been reached, and one
core is not enough Power efficiency
Smaller CPU designs can be optimized better Individual cores or processors can be turned
off when not needed
Job-level parallelism Parallel processing program
Each process can only use one core
Easier to code Most programs are
written like this Inefficient when you
have multiple cores but only one main program
Each process can have multiple threads, which run on different cores
Harder to code Used in OS, which has
many independent tasks, and in web servers, where each request can be handled separately
Best use of multiple cores
Software-rendered display represents most of the game’s CPU usage (IE more than the physics calculations), and the graphics output cannot naturally be split into multiple threads
3D hardware-accelerated graphic output is typically the performance bottleneck, and since the GPU is 50x + faster on a video card than on a CPU, multicore CPUs will not help
In games where every object can collide with every other object, physics cannot be parallelized easily because any two collisions may need to access the same memory
Every event has to happen in order, but parallel processing does not naturally do this
Sequential Concurrent
Dim Shared As Integer total
Sub program () 'this part can be done several times
at once 'because it does not depend on 'other parts of the program Dim As Integer addme = 0 For i As Integer = 1 To 10000 addme += 1 Next i 'accesses a global variable total += addmeEnd Sub
For i As Integer = 1 To 100 program()Next i
Dim Shared As Integer totalDim Shared As Any Ptr mutex
Sub program () Dim As Integer addme = 0 For i As Integer = 1 To 10000 addme += 1 Next i Mutexlock(mutex) total += addme Mutexunlock(mutex)End Sub
mutex = Mutexcreate() Dim As Any Ptr threads(1 To 100) For i As Integer = 1 To 100 threads(i) = Threadcreate(@program()) Next i For i As Integer = 1 To 100 Threadwait(threads(i)) Next iMutexdestroy(mutex)
Each processor has its own cache
If one processor changes the memory, the other processors may have the wrong data cached
Snooping protocol: when one processor changes the data, every other processor must remove (invalidate) its copy
AMD’s MOESI protocol: every cache block has data in one of these five states: modified, owned, exclusive, shared, or invalid
Adding several cores to a machine will provide limited speed improvements, because the other components have not been upgraded
In this example, adding cores allows more FLOPs, but not more data transfer
Intel is developing 6 and 8 core processors (Westmere and Nehalem)
Tilera produces 64-core chips (TILE64) with an architecture made for many cores Removes the bus data-
transfer bottleneck Saves power by
powering-off individual cores
Comes with developer tools for making parallel processing programs
CPU GPU
Slowly adopting multiple cores
Caches exploit locality
Needs low-latency RAM
Naturally better suited to parallelism, and uses major multithreading to achieve performance The GeForce 8800 GTX has
16 multiprocessors and 16 * 8 multithreaded floating-point processors
No locality; uses course-grained hardware multithreading to minimize time loss
Needs high-bandwidth RAM
Costs Benefits
Maintenance and storage costs for each machine
Operating systems will take RAM from each machine
Resources such as RAM cannot be shared well among machines
Can be built with mass-produced computers and standard LAN hardware.
Can reach sizes beyond the limits of current multicore chips
Can be spread over multiple physical locations Gives your company more
bandwidth than any one ISP offers Provides redundancy in case of fire
or power outage
Can be upgraded without replacing the current hardware
Sparse Matrix-Vector multiplication test and the Lattice-Boltzmann Magneto-Hydrodynamics test give different results
Less FLOPs per core when there are many cores Upgrading from 2 cores to 4 may have little effect
Certain processors better for certain applications (IE Xeon) Multicores demand new methods of software optimization
Computer Organization and Design: the Hardware / Software Interface, 4th ed., by David A. Patterson and John L. Hennessy
AMD.com PCLaunches.com
(New Intel Processors)
Tilera.com