Physical organization of parallel platforms

Physical Organization
of Parallel Platforms
We begin this discussion with an ideal parallel machine called Parallel
Random Access Machine, or PRAM.

Architecture of an
Ideal Parallel Computer
• A natural extension of the Random Access Machine (RAM) serial
architecture is the Parallel Random Access Machine, or PRAM.
• PRAMs consist of p processors and a global memory of unbounded
size that is uniformly accessible to all processors.
• Processors share a common clock but may execute different
instructions in each cycle.

Architecture of an
• Depending on how simultaneous memory accesses are handled,
PRAMs can be divided into four subclasses.
• Exclusive-read, exclusive-write (EREW) PRAM.
• Concurrent-read, exclusive-write (CREW) PRAM.
• Exclusive-read, concurrent-write (ERCW) PRAM.
• Concurrent-read, concurrent-write (CRCW) PRAM.

Architecture of an
• What does concurrent write mean, anyway?
• Common: write only if all values are identical.
• Arbitrary: write the data from a randomly selected processor.
• Priority: follow a predetermined priority order.
• Sum: Write the sum of all data items.

Physical Complexity of an
• Processors and memories are connected via switches.
• Since these switches must operate in O(1) time at the level of words,
for a system of p processors and m words, the switch complexity is
O(mp).
• Clearly, for meaningful values of p and m, a true PRAM is not
realizable.

Interconnection Networks
for Parallel Computers
• Interconnection networks carry data between processors and to
memory.
• Interconnects are made of switches and links (wires, fiber).
• Interconnects are classified as static or dynamic.
• Static networks consist of point-to-point communication links among
processing nodes and are also referred to as direct networks.
• Dynamic networks are built using switches and communication links.
Dynamic networks are also referred to as indirect networks.

Static and Dynamic
Classification of interconnection networks: (a) a static
network; and (b) a dynamic network.

• Switches map a fixed number of inputs to outputs.
• The total number of ports on a switch is the degree of the switch.
• The cost of a switch grows as the square of the degree of the switch,
the peripheral hardware linearly as the degree, and the packaging
costs linearly as the number of pins.

Interconnection Networks:
Network Interfaces
• Processors talk to the network via a network interface.
• The network interface may hang off the I/O bus or the memory bus.
• In a physical sense, this distinguishes a cluster from a tightly coupled
multicomputer.
• The relative speeds of the I/O and memory buses impact the
performance of the network.

Network Topologies
• A variety of network topologies have been proposed and
implemented.
• These topologies tradeoff performance for cost.
• Commercial machines often implement hybrids of multiple topologies
for reasons of packaging, cost, and available components.

Network Topologies: Buses
• Some of the simplest and earliest parallel machines used buses.
• All processors access a common bus for exchanging data.
• The distance between any two nodes is O(1) in a bus. The bus also
provides a convenient broadcast media.
• However, the bandwidth of the shared bus is a major bottleneck.
• Typical bus based machines are limited to dozens of nodes. Sun
Enterprise servers and Intel Pentium based shared-bus
multiprocessors are examples of such architectures.

Network Topologies: Buses
Bus-based interconnects (a) with no local caches; (b) with local
memory/caches.
Since much of the data accessed by processors is
local to the processor, a local memory can improve the
performance of bus-based machines.

Network Topologies: Crossbars
A completely non-blocking crossbar network connecting p
processors to b memory banks.
A crossbar network uses an p×m grid of switches to
connect p inputs to m outputs in a non-blocking manner.

Network Topologies: Crossbars
• The cost of a crossbar of p processors grows as O(p2).
• This is generally difficult to scale for large values of p.
• Examples of machines that employ crossbars include the Sun Ultra
HPC 10000 and the Fujitsu VPP500.

Network Topologies:
Multistage Networks
• Crossbars have excellent performance scalability but poor cost
scalability.
• Buses have excellent cost scalability, but poor performance scalability.
• Multistage interconnects strike a compromise between these
extremes.

Network Topologies:
Multistage Networks
The schematic of a typical multistage interconnection network.

Network Topologies: Multistage Omega Network
• One of the most commonly used multistage
interconnects is the Omega network.
• This network consists of log p stages, where p is
the number of inputs/outputs.
• At each stage, input i is connected to output j if:

Network Topologies:
Multistage Omega Network
Each stage of the Omega network implements a perfect
shuffle as follows:
A perfect shuffle interconnection for eight inputs and outputs.

Network Topologies:
• The perfect shuffle patterns are connected using
2×2 switches.
• The switches operate in two modes – crossover or
passthrough.
Two switching configurations of the 2 × 2 switch:
(a) Pass-through; (b) Cross-over.

Network Topologies:
A complete omega network connecting eight inputs and eight outputs.
An omega network has p/2 × log p switching nodes,
and the cost of such a network grows as (p log p).
A complete Omega network with the perfect shuffle
interconnects and switches can now be illustrated:

Network Topologies:
Multistage Omega Network – Routing
• Let s be the binary representation of the source and d be that of the
destination processor.
• The data traverses the link to the first switching node. If the most
significant bits of s and d are the same, then the data is routed in
pass-through mode by the switch else, it switches to crossover.
• This process is repeated for each of the log p switching stages.
• Note that this is not a non-blocking switch.

Network Topologies:
Multistage Omega Network – Routing
An example of blocking in omega network: one of the messages
(010 to 111 or 110 to 100) is blocked at link AB.

Network Topologies:
Completely Connected Network
• Each processor is connected to every other processor.
• The number of links in the network scales as O(p2).
• While the performance scales very well, the hardware complexity is
not realizable for large values of p.
• In this sense, these networks are static counterparts of crossbars.

Network Topologies: Completely Connected and Star
Connected Networks
Example of an 8-node completely connected network.
(a) A completely-connected network of eight nodes;
(b) a star connected network of nine nodes.

Network Topologies:
Star Connected Network
• Every node is connected only to a common node at the center.
• Distance between any pair of nodes is O(1). However, the central
node becomes a bottleneck.
• In this sense, star connected networks are static counterparts of
buses.

Network Topologies:
Linear Arrays, Meshes, and k-d Meshes
• In a linear array, each node has two neighbors, one to its left and one
to its right. If the nodes at either end are connected, we refer to it as
a 1-D torus or a ring.
• A generalization to 2 dimensions has nodes with 4 neighbors, to the
north, south, east, and west.
• A further generalization to d dimensions has nodes with 2d
neighbors.
• A special case of a d-dimensional mesh is a hypercube. Here, d = log
p, where p is the total number of nodes.

Network Topologies: Linear Arrays
Linear arrays: (a) with no wraparound links; (b) with
wraparound link.

Network Topologies:
Two- and Three Dimensional Meshes
Two and three dimensional meshes: (a) 2-D mesh with no
wraparound; (b) 2-D mesh with wraparound link (2-D torus); and
(c) a 3-D mesh with no wraparound.

Network Topologies:
Hypercubes and their Construction
Construction of hypercubes from hypercubes of lower
dimension.

Network Topologies:
Properties of Hypercubes
• The distance between any two nodes is at most log p.
• Each node has log p neighbors.
• The distance between two nodes is given by the number of bit
positions at which the two nodes differ.

Network Topologies: Tree-Based Networks
Complete binary tree networks: (a) a static tree network; and (b)
a dynamic tree network.

Network Topologies: Tree Properties
• The distance between any two nodes is no more than 2logp.
• Links higher up the tree potentially carry more traffic than those at
the lower levels.
• For this reason, a variant called a fat-tree, fattens the links as we go
up the tree.
• Trees can be laid out in 2D with no wire crossings. This is an attractive
property of trees.

Network Topologies: Fat Trees
A fat tree network of 16 processing nodes.

Evaluating
Static Interconnection Networks
• Diameter: The distance between the farthest two nodes in the
network. The diameter of a linear array is p − 1, that of a mesh is 2(
− 1), that of a tree and hypercube is log p, and that of a completely
connected network is O(1).
• Bisection Width: The minimum number of wires you must cut to
divide the network into two equal parts. The bisection width of a
linear array and tree is 1, that of a mesh is , that of a hypercube is
p/2 and that of a completely connected network is p2/4.
• Cost: The number of links or switches (whichever is asymptotically
higher) is a meaningful measure of the cost. However, a number of
other factors, such as the ability to layout the network, the length of
wires, etc., also factor in to the cost.

Evaluating
Static Interconnection Networks
Network Diameter
Bisection
Width
Arc
Connectivity
Cost
(No. of links)
Completely-connected
Star
Complete binary tree
Linear array
2-D mesh, no wraparound
2-D wraparound mesh
Hypercube
Wraparound k-ary d-cube

Evaluating Dynamic Interconnection Networks
Network Diameter
Bisection
Width
Arc
Connectivity
Cost
(No. of links)
Crossbar
Omega Network
Dynamic Tree

Cache Coherence
in Multiprocessor Systems
• Interconnects provide basic mechanisms for data transfer.
• In the case of shared address space machines, additional hardware is
required to coordinate access to data that might have multiple copies
in the network.
• The underlying technique must provide some guarantees on the
semantics.
• This guarantee is generally one of serializability, i.e., there exists some
serial order of instruction execution that corresponds to the parallel
schedule.

Cache Coherence
in Multiprocessor Systems
Cache coherence in multiprocessor systems: (a) Invalidate protocol; (b)
Update protocol for shared variables.
When the value of a variable is changes, all its
copies must either be invalidated or updated.

Cache Coherence:
Update and Invalidate Protocols
• If a processor just reads a value once and does not need it again, an
update protocol may generate significant overhead.
• If two processors make interleaved test and updates to a variable, an
update protocol is better.
• Both protocols suffer from false sharing overheads (two words that
are not shared, however, they lie on the same cache line).
• Most current machines use invalidate protocols.

Maintaining Coherence
Using Invalidate Protocols
• Each copy of a data item is associated with a state.
• One example of such a set of states is, shared, invalid, or dirty.
• In shared state, there are multiple valid copies of the data item (and
therefore, an invalidate would have to be generated on an update).
• In dirty state, only one copy exists and therefore, no invalidates need
to be generated.
• In invalid state, the data copy is invalid, therefore, a read generates a
data request (and associated state changes).

State diagram of a simple three-state coherence protocol.

Example of parallel program execution with the simple
three-state coherence protocol.

Snoopy Cache Systems
How are invalidates sent to the right processors?
In snoopy caches, there is a broadcast media that
listens to all invalidates and read requests and performs
appropriate coherence operations locally.
A simple snoopy bus based cache coherence system.

Performance of Snoopy Caches
• Once copies of data are tagged dirty, all subsequent operations can
be performed locally on the cache without generating external traffic.
• If a data item is read by a number of processors, it transitions to the
shared state in the cache and all subsequent read operations become
local.
• If processors read and update data at the same time, they generate
coherence requests on the bus - which is ultimately bandwidth
limited.

Directory Based Systems
• In snoopy caches, each coherence operation is sent to all processors.
This is an inherent limitation.
• Why not send coherence requests to only those processors that need
to be notified?
• This is done using a directory, which maintains a presence vector for
each data item (cache line) along with its global state.

Directory Based Systems
Architecture of typical directory based systems: (a) a centralized
directory; and (b) a distributed directory.

Performance of
Directory Based Schemes
• The need for a broadcast media is replaced by the directory.
• The additional bits to store the directory may add significant
overhead.
• The underlying network must be able to carry all the coherence
requests.
• The directory is a point of contention, therefore, distributed directory
schemes must be used.

Physical organization of parallel platforms

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Physical organization of parallel platforms