Exploiting Superword Level Parallelism with Multimedia Instruction

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First found May 22, 2018

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1
Versatile Tiled-Processor Architectures
The Raw Approach
Rodric M. Rabbah
with Ian Bratt, Krste Asanovic,
Anant Agarwal
Processor Model
• Stable model for last few decades
– Von Neumann architecture
– Sequentially execute instructions
– Simple abstraction
– Easy to program
2
Change Is Around the Corner
• Processor performance not scaling as
before
– Wire delay and power
old view: chip looks small to a wire
chip size
distance signal can travel
in 1 cycle
new view: chip looks much bigger to a wire,
communication is expensive even on chip!
• How to effectively use transistors?
3
Spatially-Aware Architectures
• Many forward looking architectures are
addressing the physical challenges
– MIT Raw processor
– MIT Scale processor
– Stanford Imagine processor
– Stanford Smart Memories processor
– UC David Synchroscalar
– UT Austin TRIPS processor
– Wisconsin ILDP architecture
– The original IBM BlueGene processor
4
Problems with Monolithic Designs
• Super-wide general purpose processors are
no longer practical
ALU
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Bypass Net
Unified
Load/Store
Queue
ALU
RF
ALU
Wide
Fetch
(16 inst)
ALU
• Area,
power, and
frequency
concerns
PC
control
• Centralized
control
with global
operand
routing
5
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Bypass Net
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Spatial Architectures
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Bypass Net
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Spatial Architectures
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Spatial Architectures
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RF
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>>
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+
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Exploiting Locality
9
Raw On-Chip Networks
• 2 Static Networks
– Software configurable crossbar
– 3 cycle latency for nearestneighbor ALU to ALU
– Must know pattern at compile-time
– Flow controlled
• 2 Dynamic Networks
Computation
Resources
– Header encodes destination
– Fire and Forget
– 15 cycle latency for nearest-neighbor
Switch
Processor
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Distribute the Register File
RF
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Distribute the Rest
ALU
I$
Unified
Load/Store D$
Queue
RF
PC
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PC
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D$
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Control
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Wide
Fetch
(16 inst)
PC
ALU
I$
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PC
RF
ALU
PC
12
Tiled-Processor Architecture
D$
PC
I$
D$
RF
PC
I$
D$
ALU
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PC
I$
D$
RF
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PC
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PC
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PC
13
Tiled-Processor Architecture
• Tile abstraction is quite powerful
– e.g., power → resources used as
necessary
• Easily scalable
• All signals registered at tile
boundaries, no global signals
– Easier to Tune the Frequency
– Easier to do the Physical Design
– Easier to Verify
14
Close-up of a Single Raw Tile
Static Router
Fetch Unit
Compute
Processor
Fetch Unit
Compute
Processor
Data Cache
15
The MIT Raw Processor
• 180 nm ASIC
(IBM SA-27E)
• 16 tiles → 16 issue
• Core Frequency:
425 MHz @ 1.8 V
500 MHz @ 2.2 V
• Frequency competitive
with IBM-implemented
PowerPCs in same
process
• 18 W (vpenta)
16
The Raw Goal
• Create an architecture that
– Scales to 100’s-1000’s of functional units,
memory ports
• By exploiting custom-chip like features
– Application-specific routing of operands
– Is “general purpose” (Versatile )
• Run ILP sequential programs, scientific
computations, server-style processing, streaming
systems, and bit-level applications
• Support standard General Purpose Abstractions
– Context switching, caching and instruction virtualization
17
The New Performance Goal
18
Performance
Versatility
Selectable Virtual
Machines
DSP
Desktop Server
Throughput Stream
ILP
ASIC
Bit-level
Architecture and Application Space
• Raw architecture as an “all-purpose” processor
– Better SPECmark/Watt across the board
• Higher SPECmark → think more MIPS compared to some
reference machine (e.g., VAX 11/780)
Figure borrowed from DARPA PCA Forum
Application Domains
• 5 market-dominant application domains
– Desktop Integer
– Desktop Floating (Scientific codes)
– Server (Throughput Based)
• Ergonomic simulations, Grid computation,
Transaction processing
– Embedded Streaming
– Embedded Bit-Level
19
How Applications Differ
streaming
data spatial locality
bit-level
desktop
floating point
(scientific)
server
data temporal locality
desktop
integer
20
Distinguishing Application Domains
• Five basis properties
– Data temporal locality
• Quantify address reuse
– Spatial temporal locality
• Quantify address adjacency
– Predominant data type
– Parallelism
• ILP, DLP, TLP, etc
– Instruction temporal locality
• Inverse of control complexity
21
Classifying Applications
22
• Quantitative metrics for the basis
properties
– Measure properties of different applications
• Cluster applications into domains
– VersaBench
Data Type
Parallelism
Instruction
Temporal
Locality
Data
Temporal
Locality
Data
Spatial
Locality
VersaBench Status
• 15 total benchmarks
– 3 per category
• Drawn from SPEC INT/FP, Raw, StreamIt, DIS
(AAEC), USC ISI
– Manageable size, encourages evaluation using
the entire suite
– Available online at
http://cag.csail.mit.edu/versabench
• Benchmarks selected systematically
– MIT Technical Memo 646, June 2004
Rabbah, Bratt, Asanovic, Agarwal
23
Proposed Metric: Versatility
Versatility (VersaBench)
Geometric Mean of Speedup relative to best performing machines
SPECmark (SPEC)
Geometric Mean of Speedup relative to a single reference machine
• Normalization to the best performing machines
identifies areas for improvements
– This is especially important → VersaGraphs
– Not another mean over N benchmarks
• High Versatility mark implies architecture
is good across the board
24
VersaGraph Example
speedup relative
to best machine
speedup of an ideal machine
desktop desktop server
integer float
stream bit-level
25
speedup relative
to best machine
VersaGraph Example
speedup of a general
purpose machine (e.g., P3)
desktop desktop server
integer float
stream bit-level
26
VersaGraph Example
speedup relative
to best machine
speedup of an ASIC
desktop desktop server
integer float
stream bit-level
27
VersaGraphs For Real Architectures
Also compared against Athlon 64 and Itanium 2
P4 (2.8 GHz)
Raw (425 MHz)
P3 (600 MHz)
integer
mcf, parser
scientific
bmm, vpenta
server
mgrid, dbms
stream
corner, radio
bit-level
80211a, 8b10b
28
29
Raw Homepage
http://cag.csail.mit.edu/raw
download papers, benchmarks, …

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