Fit For Purpose Platform Architecture Selection and Design

Transcription

1 Fit For Purpose Platform Architecture Selection and Design Aug 6, 2012 AM Session Joe Temple IBM

2 What is Fit for Purpose (F4P)? Fit for Purpose is a client centric thought process that when applied, yields infrastructure architecture decisions which are in line with the client s requirements and local conditions. It is based on the fundamental principles that one size does not fit all and that local factors matter. Scale Time Horizon ISV Support Deployment Model Non-functional Requirements Technology Adoption System z System x Geographic Considerations Architecture Power Environmental Constraints Politics Strategic Direction Skills TCO Model

3 A Client s Decision Matrix Transaction volumes Data integrity/ value High Availability High Medium High z Data volumes High Medium Medium Low Low Low Medium X86 Low Low High Medium High Recovery Longevity of solution > 5 years 3 to 5 years Low Medium Medium Low High Cost of acquisition Compute / Memory intensity

4 Know the legacy, workload, and costs Know the current IT Environment Understand the workload Examine costs

5 Fit for Purpose Categorized Workloads Mixed Workload Type 1 Scales up Updates to shared data and work queues Complex virtualization Business Intelligence with heavy data sharing and ad hoc queries Parallel Data Structures Type 3 Scales well on clusters XML parsing Buisness intelligence with Structured Queries HPC applications Application Function Data Structure Usage Pattern SLA Integration Scale Highly Threaded Type 2 Scales well on large SMP Web application servers Single instance of an ERP system Some partitioned databases Small Discrete Type 4 Limited scaling needs HTTP servers File and print FTP servers Small end user apps Black are design factors Blue are local factors

6 Using Pfister s paradigm we can map the workload types to our existing platforms and new platforms we build as a result of a WOS study p775

7 Pfisters Paradigm and the Trends Contention and Coherence Single Threaded Work with clustering and replication Single Threaded Loads Parallel Nirvana Parallel Hell Scaled up OLTP and Mixed Loads Query Loads with data updates with structure Parallel Purgatory Data Load Saturation

8 Workload Optimization Serial Fitness - Clk * SMT Take Down There is a clear trade off at work here: Workload Optimization 8 Socket Machines Bubble Size is Parallel Fitness - Thread Count Data Fitness - Cache/Thread To have more threads you must give up thread speed and cache/thread Machine capacity metrics govern how that tradeoff is made. In turn the metrics are designed for the style of computing used by each machine s base market. z196 p780 ST p780 SMT2 p780 SMT4 p780t ST p780t SMT2 p780t SMT4 X7560 ST X7560 HT X7542

9 'Socket'Designs'in'Fitness'Space' Bubble'Size'is'threads' Thread'Speed' 1.2" 1" 0.8" 0.6" 0.4" 0.2" 0" )0.5" 0" 0.5" 1" 1.5" Cache/Thread' Super"Cluster" Exadata"2)2" Exadata"8)2" Power"780"SMT4" Power"780T"SMT2" Power"780T"ST" z196" Sandy"Bridge"

10 Throughput and Capacity Relating Fitness to workloads We observe: Throughput ~ Thread Count x Thread Speed Also: Thread Capacity ~ Cache / Thread x Thread Speed We Assert: Performance ~ Thread Capacity and Throughput We Define: Performance = w(thread Capacity) +(1-w)(Throughput)

11 Machines have different Throughput and Thread Capacity 1" 0.9" 0.8" 0.7" 0.6" 0.5" 0.4" 0.3" 0.2" 0.1" 0" 0" 0.2" 0.4" 0.6" 0.8" 1" Super"Cluster" Exadata"2:2" Exadata"8:2" Sandy"Bridge" Power"780"SMT4" Power"780T"SMT2" Power"780T"ST" z196" Envelope" Note that Very High Throughput à Very Low Thread Capacity (and Vice Versa) Therefore to achieve Very High Throughput workload must have Very Low Weight

12 Single Core Relative Capacity Sandy&Bridge&Core&per&Core& 9" 8" 7" 6" 5" 4" 3" 2" 1" 0" 0" 0.2" 0.4" 0.6" 0.8" 1" 1.2" weight& Sandy"Bridge" Power"7"ITE" Power"7" Power"7T" z196" Note that relative capacity is not linear with weight.

13 Local factors take the form of Operational Trade offs Operations governed by Normalized Headroom HR = (1-u)/u = c 2 Nt 0 /t wait HR(avg) = kcn 2 => (SLA)(Variability)(Scaling) U = 1/(1+HR) t = t 0 + t wait t wait = (t 0 )(c 2 N)(u/(1-u)) = (t 0 )(c 2 N)/HR = (1/weighted capacity)(variability)(scaling)/hr M/G/1 system T 0 ~ 1/Capacity

14 What are you Optimizing?

15 IBM Integrated Systems cover the fitness space and key legacies zenterprise Choose based on Fit z/os Linux Contention and Coherence Fitness for single threaded work Windows Linux Solaris SC Windows AIX Linux Solaris System x IBM BladeCenter New Nehalem Scalable Blades Windows AIX Linux Linux Solaris Solaris i5/os Linux AIX Exadata System x Pure Systems Enterprise Power Power775 Fitness for Data Centric Work Saturation Federation bridges gaps and also avoids stretching processor brands

16 Positioning with Throughput and Thread Capacity SC Thread Today Pure Systems is a match for workloads with lower weight.!