Transcript WildFire: A Scalable Path for SMPs

WildFire: A Scalable Path
for SMPs
Erick Hagersten and Michael Koster
Sun Microsystems Inc.
Presented by Terry Arnold II
Introduction
What was the goal?
 How did they achieve it?
 CMR
 HAS
 Competitive Comparisons
 Results
 Questions

The Goal
In the past people have been skeptical
about the ability of SMPs to continue to
scale due to their bandwidth limitations
 The trend has been to switch to cc-NUMA
 To improve the scalability of SMP
technologies

Cc-NUMA issues
Great scalability but have less than
optimal “access patterns”
 Require high software optimization for
capacity and conflict misses
 Non trivial scheduling, etc. (resource and
memory management)

How?
The answer is the same as the answer to
all engineering problems, that is, throwing
new acronyms at the problem
 Coherent Memory Replication (CMR)
 Hierarchical Affinity Scheduling (HAS)
 Both of these exploit locality as a means
of increasing performance (that is for
OLTP workloads)

The Overview
The Acronyms: CMR





S-COMA with fixed home locations for each
address
Shadow physical pages
Coherence at hardware level (64 byte)
Things start out cc-NUMA and changed into CMR
based on hardware counters that monitor
memory access patterns
Limitations – memory-resident pages and large
physical pages can only be replicated explicitly
The Acronyms: HAS
Schedules in the following way:
 Last processor it ran on
 Same node processor
 Remote node processor (when load
balances exceeds “threshold”)

Implementation
2 ASICs – NIAC (coherence), NIDC (bit
sliced interconnect)
 These improve upon latency of a switch
 NIAC – Interface and Global-Coherence
Layer
 Translators and Counters

Competition

The SGI Origin and
Sequent’s NUMA-Q
Results 1
Results 2
Questions?
Is this “solution” too dependent on the software
(kernel modifications)?
How compatible are CMR and HAS with the other DSM
solutions?