Parallel computing chapter 2

CSCI 8150
Advanced Computer Architecture

Hwang, Chapter 2
Program and Network Properties
2.4 System Interconnect Architectures

System Interconnect Architectures
Direct networks for static connections
Indirect networks for dynamic connections
Networks are used for
internal connections in a centralized system among
• processors
• memory modules
• I/O disk arrays
distributed networking of multicomputer nodes

Goals and Analysis
The goals of an interconnection network are to
provide
low-latency
high data transfer rate
wide communication bandwidth
Analysis includes
latency
bisection bandwidth
data-routing functions
scalability of parallel architecture

Network Properties and Routing
Static networks: point-to-point direct connections
that will not change during program execution
Dynamic networks:
switched channels dynamically configured to match user
program communication demands
include buses, crossbar switches, and multistage
networks
Both network types also used for inter-PE data
routing in SIMD computers

Terminology - 1
Network usually represented by a graph with a finite
number of nodes linked by directed or undirected edges.
Number of nodes in graph = network size .
Number of edges (links or channels) incident on a node =
node degree d (also note in and out degrees when edges
are directed). Node degree reflects number of I/O ports
associated with a node, and should ideally be small and
constant.
Diameter D of a network is the maximum shortest path
between any two nodes, measured by the number of links
traversed; this should be as small as possible (from a
communication point of view).

Terminology - 2
Channel bisection width b = minimum number of edges cut
to split a network into two parts each having the same
number of nodes. Since each channel has w bit wires, the
wire bisection width B = bw. Bisection width provides good
indication of maximum communication bandwidth along the
bisection of a network, and all other cross sections should
be bounded by the bisection width.
Wire (or channel) length = length (e.g. weight) of edges
between nodes.
Network is symmetric if the topology is the same looking
from any node; these are easier to implement or to
program.
Other useful characterizing properties: homogeneous
nodes? buffered channels? nodes are switches?

Data Routing Functions
Shifting
Rotating
Permutation (one to one)
Broadcast (one to all)
Multicast (many to many)
Personalized broadcast (one to many)
Shuffle
Exchange
Etc.

Permutations
Given n objects, there are n ! ways in which they
can be reordered (one of which is no reordering).
A permutation can be specified by giving the rule fo
reordering a group of objects.
Permutations can be implemented using crossbar
switches, multistage networks, shifting, and
broadcast operations. The time required to
perform permutations of the connections between
nodes often dominates the network performance
when n is large.

Perfect Shuffle and Exchange
Stone suggested the special permutation that
entries according to the mapping of the k-bit
binary number a b … k to b c … k a (that is,
shifting 1 bit to the left and wrapping it around to
the least significant bit position).
The inverse perfect shuffle reverses the effect of
the perfect shuffle.

Hypercube Routing Functions
If the vertices of a n-dimensional cube are labeled
with n-bit numbers so that only one bit differs
between each pair of adjacent vertices, then n
routing functions are defined by the bits in the node
(vertex) address.
For example, with a 3-dimensional cube, we can
easily identify routing functions that exchange data
between nodes with addresses that differ in the
least significant, most significant, or middle bit.

Factors Affecting Performance
Functionality – how the network supports data routing,
interrupt handling, synchronization, request/message
combining, and coherence
Network latency – worst-case time for a unit message to be
transferred
Bandwidth – maximum data rate
Hardware complexity – implementation costs for wire, logic,
switches, connectors, etc.
Scalability – how easily does the scheme adapt to an
increasing number of processors, memories, etc.?

Static Networks
Linear Array
Ring and Chordal Ring
Barrel Shifter
Tree and Star
Fat Tree
Mesh and Torus

Static Networks – Linear Array
N nodes connected by n-1 links (not a bus);
segments between different pairs of nodes can be
used in parallel.
Internal nodes have degree 2; end nodes have
degree 1.
Diameter = n-1
Bisection = 1
For small n, this is economical, but for large n, it is
obviously inappropriate.

Static Networks – Ring, Chordal Ring
Like a linear array, but the two end nodes are
connected by an n th link; the ring can be uni- or bi-
directional. Diameter is n/2 for a bidirectional
ring, or n for a unidirectional ring.
By adding additional links (e.g. “chords” in a circle),
the node degree is increased, and we obtain a
chordal ring. This reduces the network diameter.
In the limit, we obtain a fully-connected network,
with a node degree of n -1 and a diameter of 1.

Static Networks – Barrel Shifter
Like a ring, but with additional links between all
pairs of nodes that have a distance equal to a
power of 2.
With a network of size N = 2n, each node has
degree d = 2n -1, and the network has diameter D
= n /2.
Barrel shifter connectivity is greater than any
chordal ring of lower node degree.
Barrel shifter much less complex than fully-
interconnected network.

Static Networks – Tree and Star
A k-level completely balanced binary tree will have
N = 2k – 1 nodes, with maximum node degree of 3
and network diameter is 2(k – 1).
The balanced binary tree is scalable, since it has a
constant maximum node degree.
A star is a two-level tree with a node degree d = N
– 1 and a constant diameter of 2.

Static Networks – Fat Tree
A fat tree is a tree in which the number of edges
between nodes increases closer to the root (similar
to the way the thickness of limbs increases in a
real tree as we get closer to the root).
The edges represent communication channels
(“wires”), and since communication traffic
increases as the root is approached, it seems
logical to increase the number of channels there.

Static Networks – Mesh and Torus
Pure mesh – N = n k nodes with links between each
adjacent pair of nodes in a row or column (or higher
degree). This is not a symmetric network; interior node
degree d = 2k, diameter = k (n – 1).
Illiac mesh (used in Illiac IV computer) – wraparound is
allowed, thus reducing the network diameter to about half
that of the equivalent pure mesh.
A torus has ring connections in each dimension, and is
symmetric. An n × n binary torus has node degree of 4 and
a diameter of 2 × n / 2 .

Static Networks – Systolic Array
A systolic array is an arrangement of processing
elements and communication links designed
specifically to match the computation and
communication requirements of a specific
algorithm (or class of algorithms).
This specialized character may yield better
performance than more generalized structures, but
also makes them more expensive, and more
difficult to program.

Static Networks – Hypercubes
A binary n-cube architecture with N = 2n nodes
spanning along n dimensions, with two nodes per
dimension.
The hypercube scalability is poor, and packaging is
difficult for higher-dimensional hypercubes.

Static Networks – Cube-connected
Cycles
k-cube connected cycles (CCC) can be created
from a k-cube by replacing each vertex of the k-
dimensional hypercube by a ring of k nodes.
A k-cube can be transformed to a k-CCC with k ×
2k nodes.
The major advantage of a CCC is that each node
has a constant degree (but longer latency) than in
the corresponding k-cube. In that respect, it is
more scalable than the hypercube architecture.

Static Networks – k-ary n-Cubes
Rings, meshes, tori, binary n-cubes, and Omega
networks (to be seen) are topologically isomorphic
to a family of k-ary n-cube networks.
n is the dimension of the cube, and k is the radix,
or number of of nodes in each dimension.
The number of nodes in the network, N, is k n.
Folding (alternating nodes between connections)
can be used to avoid the long “end-around” delays
in the traditional implementation.

Static Networks – k-ary n-Cubes
The cost of k-ary n-cubes is dominated by the
amount of wire, not the number of switches.
With constant wire bisection, low-dimensional
networks with wider channels provide lower
latecny, less contention, and higher “hot-spot”
throughput than higher-dimensional networks with
narrower channels.

Network Throughput
Network throughput – number of messages a network can
handle in a unit time interval.
One way to estimate is to calculate the maximum number of
messages that can be present in a network at any instant
(its capacity); throughput usually is some fraction of its
capacity.
A hot spot is a pair of nodes that accounts for a
disproportionately large portion of the total network traffic
(possibly causing congestion).
Hot spot throughput is maximum rate at which messages
can be sent between two specific nodes.

Minimizing Latency
Latency is minimized when the network radix k and
dimension n are chose so as to make the
components of latency due to distance (# of hops)
and the message aspect ratio L / W (message
length L divided by the channel width W )
approximately equal.
This occurs at a very low dimension. For up to
1024 nodes, the best dimension (in this respect) is
2.

What is Dynamic Network
Dynamic Network is the network that can connect any input
to any output by enabling or disabling some switches in the
network
Examples:
- Shared Bus: The bus arbiter connects a processor to a
memory
- Multistage Network: Consists of several stages of
switches that are enabled to get connections
- Crossbar: Consists of a lot of switching elements, which
can be enabled to connect many inputs to many outputs
simultaneously
- The nodes in static networks (like Mesh) also consist of
dynamic crossbars

Dynamic Networks – Bus Systems
A bus system (contention bus, time-sharing bus) has
a collection of wires and connectors
multiple modules (processors, memories, peripherals, etc.) which
connect to the wires
data transactions between pairs of modules
Bus supports only one transaction at a time.
Bus arbitration logic must deal with conflicting requests.
Lowest cost and bandwidth of all dynamic schemes.
Many bus standards are available.

A Bus Connected multiprocessor system

bus

Dynamic Networks – Switch Modules
An a × b switch module has a inputs and b outputs.
A binary switch has a = b = 2.
It is not necessary for a = b, but usually a = b = 2k,
for some integer k>=1.
In general, any input can be connected to one or
more of the outputs. However, multiple inputs may
not be connected to the same output.
When only one-to-one mappings are allowed, the
switch is called a crossbar switch.

Multistage Networks
In general, any multistage network is comprised of a
collection of a × b switch modules and fixed network
modules. The a × b switch modules are used to provide
variable permutation or other reordering of the inputs, which
are then further reordered by the fixed network modules.
A generic multistage network consists of a sequence
alternating dynamic switches (with relatively small values
for a and b) with static networks (with larger numbers of
inputs and outputs). The static networks are used to
implement interstage connections (ISC).

Omega Network
A 2 × 2 switch can be configured for
Straight-through
Crossover
Upper broadcast (upper input to both outputs)
Lower broadcast (lower input to both outputs)
(No output is a somewhat vacuous possibility as well)
With four stages of eight 2 × 2 switches, and a static perfect
shuffle for each of the four ISCs, a 16 by 16 Omega
network can be constructed (but not all permutations are
possible).
In general , an n-input Omega network requires log 2 n
stages of 2 × 2 switches and n / 2 switch modules.

Baseline Network
A Baseline network can be generated recursively.

First stage contains N*N block and second stage
contains N/2*N/2 sub blocks, labeled C0, C1 and
go on until sub blocks size reached 2*2.

Crossbar Networks
A m × n crossbar network can be used to provide a
constant latency connection between devices; it can be
thought of as a single stage switch.
Different types of devices can be connected, yielding
different constraints on which switches can be enabled.
With m processors and n memories, one processor may be able to
generate requests for multiple memories in sequence; thus several
switches might be set in the same row.
For m × m interprocessor communication, each PE is connected to
both an input and an output of the crossbar; only one switch in each
row and column can be turned on simultaneously. Additional
control processors are used to manage the crossbar itself.

Summary: Notes

Bus n processors, bus width w
Multistage Network n × n network using k × k switches, line
width w
Crossbar n × n crossbar, with line width w

Summary: Minimum Latency

Bus Constant
Multistage Network O(logk n)
Crossbar Constant

Summary: Bandwidth per Processor

Bus O(w/n) to O(w)
Multistage Network O(w) to O(nw)
Crossbar O(w) to O(nw)

Summary: Wiring Complexity

Bus O(w)
Multistage Network O(nw logk n)
Crossbar O(n2w)

Summary: Switching Complexity

Bus O(n)
Multistage Network O(n logk n)
Crossbar O(n2)

Summary: Connectivity and Routing

Bus One to one, and only one at a time
Multistage Network Some permutations and broadcast (if
network “unblocked”)
Crossbar All permutations, one at a time

Parallel computing chapter 2

Recommended

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Parallel computing chapter 2 (20)

More from Md. Mahedi Mahfuj (18)

Recently uploaded (20)

Parallel computing chapter 2