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18-742 Parallel Computer Architecture Lecture 11: Caching in Multi-Core Systems

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18-742 Parallel Computer Architecture Lecture 11: Caching in Multi-Core Systems Prof. Onur Mutlu and Gennady Pekhimenko Carnegie Mellon University – PowerPoint PPT presentation

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Title: 18-742 Parallel Computer Architecture Lecture 11: Caching in Multi-Core Systems


1
18-742 Parallel Computer ArchitectureLecture
11 Caching in Multi-Core Systems
  • Prof. Onur Mutlu and Gennady Pekhimenko
  • Carnegie Mellon University
  • Fall 2012, 10/01/2012

2
Review Multi-core Issues in Caching
  • How does the cache hierarchy change in a
    multi-core system?
  • Private cache Cache belongs to one core
  • Shared cache Cache is shared by multiple cores

L2 CACHE
L2 CACHE
3
Outline
  • Multi-cores and Caching Review
  • Utility-based partitioning
  • Cache compression
  • Frequent value
  • Frequent pattern
  • Base-Delta-Immediate
  • Main memory compression
  • IBM MXT
  • Linearly Compressed Pages (LCP)

4
Review Shared Caches Between Cores
  • Advantages
  • Dynamic partitioning of available cache space
  • No fragmentation due to static partitioning
  • Easier to maintain coherence
  • Shared data and locks do not ping pong between
    caches
  • Disadvantages
  • Cores incur conflict misses due to other cores
    accesses
  • Misses due to inter-core interference
  • Some cores can destroy the hit rate of other
    cores
  • What kind of access patterns could cause this?
  • Guaranteeing a minimum level of service (or
    fairness) to each core is harder (how much space,
    how much bandwidth?)
  • High bandwidth harder to obtain (N cores ? N
    ports?)

5
Shared Caches How to Share?
  • Free-for-all sharing
  • Placement/replacement policies are the same as a
    single core system (usually LRU or pseudo-LRU)
  • Not thread/application aware
  • An incoming block evicts a block regardless of
    which threads the blocks belong to
  • Problems
  • A cache-unfriendly application can destroy the
    performance of a cache friendly application
  • Not all applications benefit equally from the
    same amount of cache free-for-all might
    prioritize those that do not benefit
  • Reduced performance, reduced fairness

6
Problem with Shared Caches
Processor Core 1
Processor Core 2
?t1
L1
L1
L2

7
Problem with Shared Caches
Processor Core 1
Processor Core 2
t2?
L1
L1
L2

8
Problem with Shared Caches
Processor Core 1
Processor Core 2
?t1
t2?
L1
L1
L2

t2s throughput is significantly reduced due to
unfair cache sharing.
9
Controlled Cache Sharing
  • Utility based cache partitioning
  • Qureshi and Patt, Utility-Based Cache
    Partitioning A Low-Overhead, High-Performance,
    Runtime Mechanism to Partition Shared Caches,
    MICRO 2006.
  • Suh et al., A New Memory Monitoring Scheme for
    Memory-Aware Scheduling and Partitioning, HPCA
    2002.
  • Fair cache partitioning
  • Kim et al., Fair Cache Sharing and Partitioning
    in a Chip Multiprocessor Architecture, PACT
    2004.
  • Shared/private mixed cache mechanisms
  • Qureshi, Adaptive Spill-Receive for Robust
    High-Performance Caching in CMPs, HPCA 2009.

10
Utility Based Shared Cache Partitioning
  • Goal Maximize system throughput
  • Observation Not all threads/applications benefit
    equally from caching ? simple LRU replacement not
    good for system throughput
  • Idea Allocate more cache space to applications
    that obtain the most benefit from more space
  • The high-level idea can be applied to other
    shared resources as well.
  • Qureshi and Patt, Utility-Based Cache
    Partitioning A Low-Overhead, High-Performance,
    Runtime Mechanism to Partition Shared Caches,
    MICRO 2006.
  • Suh et al., A New Memory Monitoring Scheme for
    Memory-Aware Scheduling and Partitioning, HPCA
    2002.

11
Utility Based Cache Partitioning (I)
  • Utility Uab Misses with a ways Misses with b
    ways

Low Utility
Misses per 1000 instructions
High Utility
Saturating Utility
Num ways from 16-way 1MB L2
12
Utility Based Cache Partitioning (II)
equake
vpr
UTIL
Misses per 1000 instructions (MPKI)
LRU
Idea Give more cache to the application that
benefits more from cache
13
Utility Based Cache Partitioning (III)
Shared L2 cache
I
I
Core1
Core2
D
D
Main Memory
  • Three components
  • Utility Monitors (UMON) per core
  • Partitioning Algorithm (PA)
  • Replacement support to enforce partitions

14
Cache Capacity
  • How to get more cache without making it
    physically larger?
  • Idea Data compression for on chip-caches

15
Base-Delta-Immediate Compression Practical Data
Compression for On-Chip Caches
  • Gennady Pekhimenko
  • Vivek Seshadri
  • Onur Mutlu , Todd C. Mowry

Phillip B. Gibbons Michael A. Kozuch

16
Executive Summary
  • Off-chip memory latency is high
  • Large caches can help, but at significant cost
  • Compressing data in cache enables larger cache at
    low cost
  • Problem Decompression is on the execution
    critical path
  • Goal Design a new compression scheme that has
  • 1. low decompression latency, 2. low cost, 3.
    high compression ratio
  • Observation Many cache lines have low dynamic
    range data
  • Key Idea Encode cachelines as a base multiple
    differences
  • Solution Base-Delta-Immediate compression with
    low decompression latency and high compression
    ratio
  • Outperforms three state-of-the-art compression
    mechanisms

17
Motivation for Cache Compression
  • Significant redundancy in data

0x00000000
0x0000000B
0x00000003
0x00000004
  • How can we exploit this redundancy?
  • Cache compression helps
  • Provides effect of a larger cache without making
    it physically larger

18
Background on Cache Compression
Hit
L2 Cache
CPU
L1 Cache
Decompression
Uncompressed
Compressed
Uncompressed
  • Key requirements
  • Fast (low decompression latency)
  • Simple (avoid complex hardware changes)
  • Effective (good compression ratio)

19
Zero Value Compression
  • Advantages
  • Low decompression latency
  • Low complexity
  • Disadvantages
  • Low average compression ratio

20
Shortcomings of Prior Work
Compression Mechanisms Decompression Latency Complexity Compression Ratio
Zero ? ? ?
21
Frequent Value Compression
  • Idea encode cache lines based on frequently
    occurring values
  • Advantages
  • Good compression ratio
  • Disadvantages
  • Needs profiling
  • High decompression latency
  • High complexity

22
Shortcomings of Prior Work
Compression Mechanisms Decompression Latency Complexity Compression Ratio
Zero ? ? ?
Frequent Value ? ? ?
23
Frequent Pattern Compression
  • Idea encode cache lines based on frequently
    occurring patterns, e.g., half word is zero
  • Advantages
  • Good compression ratio
  • Disadvantages
  • High decompression latency (5-8 cycles)
  • High complexity (for some designs)

24
Shortcomings of Prior Work
Compression Mechanisms Decompression Latency Complexity Compression Ratio
Zero ? ? ?
Frequent Value ? ? ?
Frequent Pattern ? ?/? ?
25
Shortcomings of Prior Work
Compression Mechanisms Decompression Latency Complexity Compression Ratio
Zero ? ? ?
Frequent Value ? ? ?
Frequent Pattern ? ?/? ?
Our proposal B?I ? ? ?
26
Outline
  • Motivation Background
  • Key Idea Our Mechanism
  • Evaluation
  • Conclusion

27
Key Data Patterns in Real Applications
Zero Values initialization, sparse matrices,
NULL pointers
0x00000000
0x00000000
0x00000000
0x00000000

Repeated Values common initial values, adjacent
pixels
0x000000FF
0x000000FF
0x000000FF
0x000000FF

Narrow Values small values stored in a big data
type
0x00000000
0x0000000B
0x00000003
0x00000004

Other Patterns pointers to the same memory region
0xC04039C0
0xC04039C8
0xC04039D0
0xC04039D8

28
How Common Are These Patterns?
  • SPEC2006, databases, web workloads, 2MB L2 cache
  • Other Patterns include Narrow Values
  • 43 of the cache lines belong to key patterns

29
Key Data Patterns in Real Applications
Zero Values initialization, sparse matrices,
NULL pointers
Low Dynamic Range Differences between values
are significantly smaller than the values
themselves
0x00000000
0x00000000
0x00000000
0x00000000

Repeated Values common initial values, adjacent
pixels
0x000000FF
0x000000FF
0x000000FF
0x000000FF

Narrow Values small values stored in a big data
type
0x00000000
0x0000000B
0x00000003
0x00000004

Other Patterns pointers to the same memory region
0xC04039C0
0xC04039C8
0xC04039D0
0xC04039D8

30
Key Idea BaseDelta (B?) Encoding
4 bytes
  • 32-byte Uncompressed Cache Line

0xC04039C0
0xC04039C8
0xC04039D0

0xC04039F8
0xC04039C0
  • Base

12-byte Compressed Cache Line
0x00
0x08
0x10

0x38
1 byte
1 byte
1 byte
? Fast Decompression vector addition
? Simple Hardware arithmetic and comparison
20 bytes saved
? Effective good compression ratio
31
B? Compression Ratio
  • SPEC2006, databases, web workloads, L2 2MB cache
  • Good average compression ratio (1.40)
  • But some benchmarks have low compression ratio

32
Can We Do Better?
  • Uncompressible cache line (with a single base)
  • Key idea
  • Use more bases, e.g., two instead of one
  • Pro
  • More cache lines can be compressed
  • Cons
  • Unclear how to find these bases efficiently
  • Higher overhead (due to additional bases)

0x00000000
0x09A40178
0x0000000B
0x09A4A838

33
B? with Multiple Arbitrary Bases
? 2 bases the best option based on evaluations
34
How to Find Two Bases Efficiently?
  • First base - first element in the cache line
  • Second base - implicit base of 0
  • Advantages over 2 arbitrary bases
  • Better compression ratio
  • Simpler compression logic

? BaseDelta part
? Immediate part
Base-Delta-Immediate (B?I) Compression
35
B? (with two arbitrary bases) vs. B?I
Average compression ratio is close, but B?I is
simpler
36
B?I Implementation
  • Decompressor Design
  • Low latency
  • Compressor Design
  • Low cost and complexity
  • B?I Cache Organization
  • Modest complexity

37
B?I Decompressor Design
Compressed Cache Line
?0
B0
?1
?2
?3
B0
?0
?1
?2
?3
B0
B0
B0
B0
Vector addition




V1
V2
V3
V0
V0
V1
V2
V3
Uncompressed Cache Line
38
B?I Compressor Design
32-byte Uncompressed Cache Line
8-byte B0 1-byte ? CU
8-byte B0 2-byte ? CU
8-byte B0 4-byte ? CU
4-byte B0 1-byte ? CU
4-byte B0 2-byte ? CU
2-byte B0 1-byte ? CU
Zero CU
Rep. Values CU
CFlag CCL
CFlag CCL
CFlag CCL
CFlag CCL
CFlag CCL
CFlag CCL
CFlag CCL
CFlag CCL
CFlag CCL
Compression Selection Logic (based on compr. size)
Compression Flag Compressed Cache Line
Compressed Cache Line
39
B?I Compression Unit 8-byte B0 1-byte ?
32-byte Uncompressed Cache Line
8 bytes
V0
V1
V2
V3
V0
V0
V1
V2
V3
B0
V0
B0
B0
B0
B0
-
-
-
-
?0
?1
?2
?3
Within 1-byte range?
Within 1-byte range?
Within 1-byte range?
Within 1-byte range?
Is every element within 1-byte range?
Yes
No
?0
B0
?1
?2
?3
B0
?0
?1
?2
?3
40
B?I Cache Organization
Tag Storage
Data Storage
32 bytes
Conventional 2-way cache with 32-byte cache lines


Set0

Set0

Tag0
Tag1
Set1
Data0
Data1
Set1




Way0
Way1
Way0
Way1
B?I 4-way cache with 8-byte segmented data
8 bytes
Tag Storage




Set0
Set0








Tag0
Tag1
Tag2
Tag3
Set1
S0
S0
S1
S2
S3
S4
S5
S6
S7
Set1
?C - Compr. encoding bits
C












Way0
Way1
Way2
Way3
?Twice as many tags
?Tags map to multiple adjacent segments
2.3 overhead for 2 MB cache
41
Qualitative Comparison with Prior Work
  • Zero-based designs
  • ZCA Dusser, ICS09 zero-content augmented
    cache
  • ZVC Islam, PACT09 zero-value cancelling
  • Limited applicability (only zero values)
  • FVC Yang, MICRO00 frequent value compression
  • High decompression latency and complexity
  • Pattern-based compression designs
  • FPC Alameldeen, ISCA04 frequent pattern
    compression
  • High decompression latency (5 cycles) and
    complexity
  • C-pack Chen, T-VLSI Systems10 practical
    implementation of FPC-like algorithm
  • High decompression latency (8 cycles)

42
Outline
  • Motivation Background
  • Key Idea Our Mechanism
  • Evaluation
  • Conclusion

43
Methodology
  • Simulator
  • x86 event-driven simulator based on Simics
    Magnusson, Computer02
  • Workloads
  • SPEC2006 benchmarks, TPC, Apache web server
  • 1 4 core simulations for 1 billion
    representative instructions
  • System Parameters
  • L1/L2/L3 cache latencies from CACTI Thoziyoor,
    ISCA08
  • 4GHz, x86 in-order core, 512kB - 16MB L2, simple
    memory model (300-cycle latency for row-misses)

44
Compression Ratio B?I vs. Prior Work
  • SPEC2006, databases, web workloads, 2MB L2

1.53
  • B?I achieves the highest compression ratio

45
Single-Core IPC and MPKI
3.6
16
5.6
24
4.9
5.1
21
5.2
13
19
8.1
14
  • B?I achieves the performance of a 2X-size cache

Performance improves due to the decrease in MPKI
46
Single-Core Effect on Cache Capacity
Fixed L2 cache latency
2.3
1.7
1.3
  • B?I achieves performance close to the upper bound

47
Multi-Core Workloads
  • Application classification based on
  • Compressibility effective cache size increase
  • (Low Compr. (LC) lt 1.40, High Compr. (HC) gt
    1.40)
  • Sensitivity performance gain with more cache
  • (Low Sens. (LS) lt 1.10, High Sens. (HS) gt 1.10
    512kB -gt 2MB)
  • Three classes of applications
  • LCLS, HCLS, HCHS, no LCHS applications
  • For 2-core - random mixes of each possible class
    pairs (20 each, 120 total workloads)

48
Multi-Core Workloads
49
Multi-Core Weighted Speedup
If at least one application is sensitive, then
the performance improves
  • B?I performance improvement is the highest (9.5)

50
Other Results in Paper
  • Sensitivity study of having more than 2X tags
  • Up to 1.98 average compression ratio
  • Effect on bandwidth consumption
  • 2.31X decrease on average
  • Detailed quantitative comparison with prior work
  • Cost analysis of the proposed changes
  • 2.3 L2 cache area increase

51
Conclusion
  • A new Base-Delta-Immediate compression mechanism
  • Key insight many cache lines can be efficiently
    represented using base delta encoding
  • Key properties
  • Low latency decompression
  • Simple hardware implementation
  • High compression ratio with high coverage
  • Improves cache hit ratio and performance of both
    single-core and multi-core workloads
  • Outperforms state-of-the-art cache compression
    techniques FVC and FPC

52
Linearly Compressed Pages A Main Memory
Compression Framework with Low Complexity and
Low Latency
  • Gennady Pekhimenko, Vivek Seshadri, Yoongu Kim,
  • Hongyi Xin, Onur Mutlu, Phillip B. Gibbons,
  • Michael A. Kozuch, Todd C. Mowry

53
Executive Summary
  • Main memory is a limited shared resource
  • Observation Significant data redundancy
  • Idea Compress data in main memory
  • Problem How to avoid latency increase?
  • Solution Linearly Compressed Pages (LCP)
  • fixed-size cache line granularity
    compression
  • 1. Increases capacity (69 on average)
  • 2. Decreases bandwidth consumption (46)
  • 3. Improves overall performance (9.5)

54
Challenges in Main Memory Compression
  1. Address Computation
  2. Mapping and Fragmentation
  3. Physically Tagged Caches

55
Address Computation
Cache Line (64B)
Uncompressed Page
L0
L1
L2
. . .
LN-1
Address Offset
128
0
(N-1)64
64
Compressed Page
L0
L1
L2
. . .
LN-1
Address Offset
0
?
?
?
56
Mapping and Fragmentation
Virtual Page (4kB)
Virtual Address
Physical Address
Physical Page (? kB)
Fragmentation
57
Physically Tagged Caches
Virtual Address
Core
Critical Path
Address Translation
TLB
Physical Address
tag
data
L2 Cache Lines
tag
data
tag
data
58
Shortcomings of Prior Work
Compression Mechanisms Access Latency Decompression Latency Complexity Compression Ratio
IBM MXT IBM J.R.D. 01 ? ? ? ?


59
Shortcomings of Prior Work
Compression Mechanisms Access Latency Decompression Latency Complexity Compression Ratio
IBM MXT IBM J.R.D. 01 ? ? ? ?
Robust Main Memory Compression ISCA05 ? ? ? ?

60
Shortcomings of Prior Work
Compression Mechanisms Access Latency Decompression Latency Complexity Compression Ratio
IBM MXT IBM J.R.D. 01 ? ? ? ?
Robust Main Memory Compression ISCA05 ? ? ? ?
LCP Our Proposal ? ? ? ?
61
Linearly Compressed Pages (LCP) Key Idea
Uncompressed Page (4kB 6464B)
64B
64B
64B
64B
. . .
64B
41 Compression
Exception Storage
. . .
M
E
Metadata (64B) ? (compressible)
Compressed Data (1kB)
62
LCP Overview
  • Page Table entry extension
  • compression type and size
  • extended physical base address
  • Operating System management support
  • 4 memory pools (512B, 1kB, 2kB, 4kB)
  • Changes to cache tagging logic
  • physical page base address cache line index
  • (within a page)
  • Handling page overflows
  • Compression algorithms BDI PACT12 , FPC
    ISCA04

63
LCP Optimizations
  • Metadata cache
  • Avoids additional requests to metadata
  • Memory bandwidth reduction
  • Zero pages and zero cache lines
  • Handled separately in TLB (1-bit) and in metadata
  • (1-bit per cache line)
  • Integration with cache compression
  • BDI and FPC

1 transfer instead of 4
64B
64B
64B
64B
64
Methodology
  • Simulator
  • x86 event-driven simulators
  • Simics-based Magnusson, Computer02 for CPU
  • Multi2Sim Ubal, PACT12 for GPU
  • Workloads
  • SPEC2006 benchmarks, TPC, Apache web server,
    GPGPU applications
  • System Parameters
  • L1/L2/L3 cache latencies from CACTI Thoziyoor,
    ISCA08
  • 512kB - 16MB L2, simple memory model

65
Compression Ratio Comparison
SPEC2006, databases, web workloads, 2MB L2 cache
LCP-based frameworks achieve competitive average
compression ratios with prior work
66
Bandwidth Consumption Decrease
SPEC2006, databases, web workloads, 2MB L2 cache
Better
LCP frameworks significantly reduce bandwidth
(46)
67
Performance Improvement
Cores LCP-BDI (BDI, LCP-BDI) (BDI, LCP-BDIFPC-fixed)
1 6.1 9.5 9.3
2 13.9 23.7 23.6
4 10.7 22.6 22.5
LCP frameworks significantly improve performance
68
Conclusion
  • A new main memory compression framework called
    LCP(Linearly Compressed Pages)
  • Key idea ?xed size for compressed cache lines
    within a page and fixed compression algorithm per
    page
  • LCP evaluation
  • Increases capacity (69 on average)
  • Decreases bandwidth consumption (46)
  • Improves overall performance (9.5)
  • Decreases energy of the off-chip bus (37)
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