Download Recovery-Oriented Computing

Scalar Processor Design
• Phenomenal advances in its brief lifetime of
30+ years: X2/18mo in 30yr. multi-GFLOPs
processors, inspiring and facilitating major
innovations in:
–
–
–
–
–
–
–
–
–
Embedded microcontrollers (300 millions sold in 2000)
Personal computers
(150 millions sold in 2000)
Advanced workstations
Handheld and mobile devices
Application and file servers (4 millions sold in 2000)
Web servers for the Internet
Low-cost supercomputers
Large-scale computing clusters
Well over one billion microprocessors shipped per year
• The amazing decades of the evolution of
microprocessors:
1970-1980
1980-1990
1990-2000
2000-2010
Transistor count
2K-100K
100K-1M
1M-100M
100M-2B
Clock frequency
0.1-3 MHz
3-30 MHz
30MHz-1GHz
1GHz-15GHz
Instruction/cycle
0.1 IPC
0.1-0.9 IPC
0.9-1.9 IPC
1.9-2.9 IPC
Slide 1
Scalar Processor Design
Past (Milestones):
– First electronic computer ENIAC in 1946: 18,000 vacuum
tubes, 3,000 cubic feet, 20 2-foot 10-digit registers, 5
KIPs (thousand additions per second);
– First microprocessor (a CPU on a single IC chip) Intel
4004 in 1971: 2,300 transistors, 60 KIPs, $200;
– Virtual elimination of assembly language programming
reduced the need for object-code compatibility;
– The creation of standardized, vendor-independent
operating systems, such as UNIX and its clone, Linux,
lowered the cost and risk of bringing out a new
architecture
– RISC instruction set architecture paved ways for drastic
design innovations that focused on two critical
performance techniques: instruction-level parallelism
and use of caches
Slide 2
Scalar Processor Design
Present (State of the art):
– Microprocessors approaching/surpassing 10 GFLOPS;
– A high-end microprocessor (<$10K) today is easily more
powerful than a supercomputer (>$10million) ten years
ago;
– While technology advancement contributes a sustained
annual growth of 35%, innovative computer design
accounts for another 25% annual growth rate  a factor
of 15 in performance gains!(fig1.1)
– Three different computing markets (fig. 1.3):
» Desktop Computing –- driven by price-performance (a
few hundreds through over 10K);
» Servers – availability driven (distinguished from
reliability), providing sustained high performance
» Embedded Computers – fastest growing portion of the
computer market, real-time performance driven, and
need to minimize memory and power, as well as ASIC
Slide 3
Scalar Processor Design
Present (State of the art):
– The Task of the Computer Designer:
» Instruction Set Architecture (Traditional view of
what Computer Architecture is), the boundary between
software and hardware;
» Organization, high-level aspects of a computer’s
design, such as the memory system, the bus structure,
the internal design of CPU, based on a given
instruction set architectrue;
» Hardware, the specifics of a machine, including the
detailed logic design and the packaging technology of
the machine.
Future (Technology Trends):
– A truly successful instruction set architecture (ISA)
should last for decades, however it takes an computer
architect’s acute observation and knowledge of the
rapidly changing technology, in order for the ISA to
survive and cope with such changes:
Slide 4
Scalar Processor Design
» IC logic technology: transistor count on a chip grows at
55% annual rate (35% density growth rate + 10-20% die size
growth) while device speed scales more slowly;
» Semiconductor DRAM: density grows at 60% annually while
cycle time improves very slowly (decreasing one-third in
ten years). Bandwidth per chip increases twice as fast as
latency decreases;
» Magnetic dish technology: density increases at 100% annual
rate since 1990 while access time improves at about a third
every ten years; and
» Network technology: both latency and bandwidth have been
improving, with more focus on bandwidth of late; the
increasing importance of networking has led to faster
improvement in performance than before—Internet bandwidth
doubles every year in the U.S.
» Scaling of transistor performance: while transistor density
increases quadratically with linear decrease in feature
size, transistor performance increases roughly linearly
with decrease in feature sizechallenge & opportunity for
computer designer!
» Wires and power in IC: propagation delay and power needs?
Slide 5
Instruction Set Architecture
• ISA should reflect application
characteristics:
– Desktop computing is compute-intensive,
thus focusing on features favoring Integer
and FP ops;
– Server computing is data-intensive,
focusing on integers and char-strings (yet
FP ops are still standard in them)
– Embedded computing is time-sensitive,
memory and power conciouse, thus focusing
on code-density, real-time and media data
streams.
• ISA has been defined as a contract between the
software and hardware, or between the program
and the machine, thus facilitating independent
development of programs and machines.
Slide 6
Instruction Set Architecture
• Taxonomy of ISA:
– Stack: both operands are implicit on the top of
the stack, a data structure in which items are
accessed an a last in, first out fashion.
– Accumulator: one operand is implicit in the
accumulator, a special-purpose register.
– General Purpose Register: all operands are
explicit in specified registers or memory
locations. Depending on where operands are
specified and stored, there are three different
ISA groups:
» Register-Memory: one operand in register and one in
memory.Examples: IBM 360/370, Intel 80x86 family, Mototola
68000;
» Memory-Memory: both operands are in memory. Example: VAX.
» Register=Register (load & store): all operands, except for
those in load and store instructions, are in registers.
Examples: SPARC (Sun Microsystems), MIPS, Precision
Architecture (HP), PowerPC (IBM), Alpha (DEC).
Slide 7
Instruction Set Architecture
Taxonomy of ISA: Examples
(a) Stack
(b) Accumulator
TOS
Stack
(c) Register-Memory
CA+B
(d) Reg-Reg/Load-Store (e) Memory-Memory
Reg. Set
Reg. Set
ALU
ALU
Accumulator
ALU
Memory
Push A
Push B
Add
Pop C
ALU
Memory
Load A
Add B
Store C
Memory
Load R1,A
Add R3,R1,B
Store R3,C
Memory
Load
Load
Add
Store
R1,A
R2,B
R3,R1,R2
R3,C
ALU
Memory
Add C,A,B
Slide 8
Dynamic and Static Interface
• Inherent in each ISA’s definition is an associated
definition of an interface, called the dynamic and
static interface (DSI), that separates what is done
statically at compile time versus what is done
dynamically at run time.
– A key issue in the design of an ISA is the
placement of the DSI:
Program (Software)
Compiler
complexity
Exposed to
software
Architecture
Machine (Hardware)
DEL
Hardware
complexity
~CISC
Hidden in
hardware
~VLIW
“Static”
(DSI)
“Dynamic”
~RISC
HLL Program
DSI-1
DSI-2
DSI-3
Hardware
Slide 9
Processor Performance
• Performance Equation:
– 1/Performance = Time/Program
= (Instuctions/Program)(Cycles/Instructions)(Time/Cycle)
= IC * CPI * CCT
Where: IC=Instruction Count;
CPI=Cycles per Instruction; CCT=Clock cycle time
• Performance Optimization: reducing one or more of
the three factors (IC, CPI, CCT)
– IC: ISA dependent (CISC vs RISC); compiler dependent; dynamic
elimination of redundant computation (computation reuse);
– CPI: ISA and instruction complexity dependent;
microarchitecture dependent (pipelining and speculative
execution of instructions);
– CCT: microarchitecture; pipelining; clock frequency; pipeline
depth; can affect CPI;
Slide 10
Processor Performance
• Performance Evaluation: functional (ISA) and performance (CPI)
– Trace-driven simulation
Physical execution with
software instrumentation
Physical execution with
hardware instrumentation
Traces
Traces
Functional simulator
Trace storage
Cycle-based
performance
simulator
Trace-driven
Timing simulation
Trace generation
– Execution-driven simulation
Execution trace
Functional simulator
(instruction interpretation)
Functional simulation
Checkpoint and control
Cycle-based
performance
simulator
Execution-driven
Timing simulation
Slide 11
Processor Performance
Pipeline Depth
• Performance Evaluation: Amdahl’s Law
– Idealized pipeline execution profile
1
Speedup 
(1  f ) 
f
Speedupenhance
N
1
Speedup 
(1  g ) 
1
1-g
g
N
g
– Realistic pipeline execution profile
Pipeline stall
Pipeline stall
N
1
– Refined speedup equation :
Speedup 
1
g1 g 2
gN
  ... 
1 2
N
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