Many-Core Computing Era and New Challenges. Nikos Hardavellas, EECS
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1 Many-Core Computing Era and New Challenges Nikos Hardavellas, EECS
2 Moore s Law Is Alive And Well 90nm 90nm transistor (Intel, 2005) Swine Flu A/H1N1 (CDC) 65nm nm nm nm nm 2019 Device scaling continues for at least another 10 years 2
3 Good Days Ended Nov [Yelick09] Traditional Moore s Law: 2x transistors with every generation New Moore s Law: 2x cores with every generation 3
4 What Happened in Nov. 2002? Chips Became Too Hot 1000 Nuclear Reactor Rocket Nozzle Watts/cm Hot plate Pentium 4 Pentium III Pentium II Pentium Pro i386 i486 Pentium 1 1.5m 1m 0.7m 0.5m 0.35m 0.25m 0.18m 0.13m 0.1m 0.07m [Pollack] 4
5 The New Cooking Sensation! [Huang] 5
6 But, Chips Are Physically Constrained, and Constraints Don t Scale Thermal? Area? core core core core core core L2 core core core core data data data core core Bandwidth? Power? How to balance constraints and achieve peak performance? 6
7 A Look at Technology Projections & Implications Use ITRS projections + first-order models/analysis Given a technology node, find highest-performance design Respect physical constraints Account for SW trends Amdahl s Law Exponentially-increasing datasets 7
8 Number of Cores How Many Cores/Cache Can We Power? Area Power (Max Freq.) 1 GHz, 0.27V 2.7 GHz, 0.36V 4.4 GHz, 0.45V 5.7 GHz, 0.54V 6.9 GHz, 0.63V 8 GHz, 0.72V 9 GHz, 0.81V Voltage/ Freq. Scaling (VFS) Cache Size (MB) The power wall: a power/performance trade-off 8
9 Number of Cores Pin Bandwidth: Fewer Cores, More Cache Cache Size (MB) Area Power (Max Freq.) 1 GHz, 0.27V 2.7 GHz, 0.36V 4.4 GHz, 0.45V 5.7 GHz, 0.54V 6.9 GHz, 0.63V 8 GHz, 0.72V 9 GHz, 0.81V Bandwidth (1 GHz) Bandwidth (2.7GHz) Where would the best performance be? 9
10 Performance (1000 x MIPS) Peak-Performing Designs Peak-performing design Area (Max Freq.) Cache Size (MB) Power (Max Freq.) Area+Power (VFS) Bandwidth (VFS) Peak Performance First, bandwidth constrained, then power constrained 10
11 Number of Cores Cache Size (MB) Transistor Scaling: Many Cores, Huge Caches Area (Max Freq.) 256 OLTP - Peak Perf. 128 DSS - Peak Perf. 64 Apache - Peak Perf. 32 OLTP (Max Freq.) 16 8 DSS (Max Freq.) 4 Apache (Max Freq.) OLTP 64 DSS Apache Year of Technology Introduction Year of Technology Introduction Need cache architectures for >>MB 11
12 Why Are Caches Growing So Large? Increasing number of cores: cache grows commensurately Fewer but faster cores have the same effect Increasing datasets: faster than Moore s Law! Power/thermal efficiency: caches are cool, cores are hot So, its easier to fit more cache in a power budget Limited bandwidth: large cache == more data on chip Off-chip pins are used less frequently So, caches are getting huge. Great! What s the catch? 12
13 L2 Cache Size (KB) L2 Hit Latency (cycles) Larger Caches Are Slower Caches 100,000 10,000 1, large caches Year slow access Year Does this affect the end performance? 13
14 Norm. Throughput Impact of Slower Caches on Performance DSS-const OLTP-const DSS-real OLTP-real L2 Cache Size (MB) We lose half the potential throughput! 14
15 Cache Design Trends As caches become bigger, they get slower: Divide the cache into smaller slices : Balance cache slice access with network latency 15
16 Modern Caches: Distributed core core core core Split cache into slices, distribute across die 16
17 Modern Multi-Core Chips 17
18 Multi-Core: Distributed System On Chip Similar structure Nodes in a cluster or a multiprocessor -> cores on a chip Physically distributed memory -> distributed cache Interconnect -> ditto, but on chip Similar challenges, albeit with different constants: Power, thermal, reliability, performance Also, some new challenges: Off-chip bandwidth: limited pins 18
19 Data Placement Determines Performance cache slice Goal: place data on chip close to where they are used Hardavellas
20 Terminology: Data Types core core core core core Read or Read Read Write Read Write L2 L2 L2 Private Shared Read-Only Shared Read-Write Hardavellas
21 Distributed shared L2 address mod <#slices> Unique location for any block (private or shared) Maximum capacity, but slow access (40+ cycles) Can we do better? 21
22 Distributed private L2: private data access On every access allocate data at local L2 slice Private data: allocate at local L2 slice Fast access to core-private data What about accesses to shared data? 22
23 Distributed private L2: shared-ro access On every access allocate data at local L2 slice Shared read-only data: replicate across L2 slices Wastes capacity due to replication What about accesses to shared read-write data? 23
24 Distributed private L2: shared-rw access On every access allocate data at local L2 slice L2 L2 L2 dir X Slow for shared read-write Wastes capacity (dir overhead) and bandwidth 24 Shared read-write data: maintain coherence via indirection (dir)
25 Conventional Multi-Core Caches Shared Private dir L2 L2 L2 Address-interleave blocks (sliceid = addr mod #slices) + High effective capacity Slow access We want: high capacity (shared) + fast access (priv.) 25 Each block cached locally + Fast access (local) Low capacity (replicas) Coherence: via indirection (distributed directory) 2009 Hardavellas
26 migrate Where to Place the Data? Close to where they are used! Accessed by single core: migrate locally Accessed by many cores: replicate (?) If read-only, replication is OK If read-write, coherence a problem Low reuse: evenly distribute across sharers read-write share read-only replicate sharers# Hardavellas
27 % % RW Read-Write Blocks in Blocks Bubble Cache Access Classification Example Each bubble: cache blocks shared by x cores Size of bubble proportional to % L2 accesses y axis: % blocks in bubble that are read-write Instructions Data-Private Data-Shared 120% 100% 80% 60% 40% 20% 0% -20% Number of Sharers 27 % L2 accesses 2009 Hardavellas
28 % RW Read-Write Blocks in Blocks Bubble % % RW Read-Write Blocks in Blocks Bubble migrate Instructions Data-Private Data-Shared Instructions Data-Private Data-Shared 120% 100% 80% 60% 60% 40% 20% 0% -20% Number of Sharers migrate locally Cache Access Clustering Server Apps share (addr-interleave) 28 Instructions Data-Private Data-Shared Instructions Data-Private Data-Shared 120% 100% R/W 80% 40% 20% R/O 0% -4-20% % sharers# Number of Sharers replicate Accesses naturally form 3 clusters share replicate Scientific/MP Apps 2009 Hardavellas
29 Instruction Replication Instruction working set too large for one cache slice L2 L2 L2 L2 Distribute in cluster of neighbors, replicate across Hardavellas
30 Reactive NUCA in a nutshell Classify accesses private data: like private scheme (migrate) shared data: like shared scheme (interleave) instructions: controlled replication (middle ground) To place cache blocks, we first need to classify them 30
31 Classification Granularity Per-block classification High area/power overhead (cut L2 size by half) High latency (indirection through directory) Per-page classification (utilize OS page table) Persistent structure Core accesses the page table for every access anyway (TLB) Utilize already existing SW/HW structures and events Page classification is accurate (<0.5% error) Classify entire data pages, page table/tlb for bookkeeping 31
32 Classification Mechanisms Instructions classification: all accesses from L1-I (per-block) Data classification: private/shared per-page at TLB miss Page classification is accurate (<0.5% error) Core i On 1 st access core L2 Ld A TLB Miss OS A: Private to i On access by another core Ld A TLB Miss OS A: Private to i A: Shared core L2 Core j Bookkeeping through OS page table and TLB Hardavellas
33 Page Table and TLB Extensions Persistent structure, ideal for directory Core accesses the page table for every access anyway (TLB) Pass information from the directory to the core Utilize already existing SW/HW structures and events TLB entry: P/S vpage ppage 1 bit Page table entry: P/S/I L2 id vpage ppage 2 bits log(n) Page granularity allows simple + practical HW Hardavellas
34 Data Class Bookkeeping and Lookup private data: place in local L2 slice Page table entry: P L2 id vpage ppage TLB entry: P vpage ppage shared data: place in aggregate L2 (addr interleave) Page table entry: S L2 id vpage ppage TLB entry: S vpage ppage Physical Addr.: tag L2 id cache index offset 34
35 Instructions Lookup: Rotational Interleaving RID log 2 (k) RID PC: 0xfa480 Addr size-4 clusters: each slice caches the same blocks local slice + 3 neighbors on behalf of any cluster Destination Addr RID 1 &( n 1 ) Fast access (nearest-neighbor, simple lookup) Balance access latency with capacity constraints Equal capacity pressure at overlapped slices 35
36 Coherence: No Need for HW Mechanisms at L2 Reactive NUCA placement guarantee Each R/W datum in unique & known location Shared data: addr-interleave Private data: local slice Fast access, eliminates HW overhead, SIMPLE Hardavellas
37 Speedup over Private 60% 50% 40% 30% 20% 10% Evaluation ASR (A) Shared (S) R-NUCA (R) Ideal (I) 0% -10% -20% A S R I A S R I A S R I A S R I A S R I A S R I A S R I A S R I OLTP DB2 Apache DSS Qry6 DSS Qry8 DSS Qry13 em3d OLTP Oracle MIX Private-averse workloads 37 Shared-averse workloads Delivers robust performance across workloads Shared: same for Web, DSS; 17% for OLTP, MIX Private: 17% for OLTP, Web, DSS; same for MIX 2009 Hardavellas
38 Reactive NUCA: Fast >>MB Caches Data may exhibit arbitrarily complex behaviors...but few that matter! Learn the behaviors at run time for placement Make the common case fast Fast! Near-optimal placement (within 5% of ideal) Robust (matches best alternative, or 17% better; up to 32%) Nearest-neighbor communication scalable Transparent to the user, simple design, negligible overhead BONUS: simplify hardware Eliminate HW coherence at L Hardavellas
39 Concluding Remarks The old multiprocessor/cluster is now within a single chip Still a distributed system Similar challenges, with new constants Lecture focused on performance via data placement Similar challenge: HPC systems strive to privatize data R-NUCA strives to use private caches New constants: latency, bandwidth, Single OS image allows for many simplifications Research opportunity: map ideas from old domain to new 39
40 Multicore Research Brings Opportunities and Challenges Multicore: This is the one which will have the biggest impact on us. We have never had a problem to solve like this. A breakthrough is needed in how applications are done on multicore devices. Bill Gates It s time we rethink some of the basics of computing. It s scary and lots of fun at the same time. Burton Smith 40
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