# Presentation: 448072

## One  step  ahead

**One  step  ahead**

## The Challenges of Architectures that Grow to Petascale and can be Sustained Economically

**Steve Reinhardt**
**Principal Engineer, SGI**
**spr at sgi.com**

**Notes:**
This presentation is intended to replace the previous SGI Altix Technical NDA presentation.  This revision has focused on delivering a concise introduction to the SGI Altix 3000 supplemented by technical detail to answer specific questions.

Please provide feedback to Jason Pettit (jason@sgi.com) with requests for additional technical detail, suggestions for improvement, or revisions.  All feedback is kindly appreciated.

Revision History:
10/13/03 Initial draft release

- SGI’s systems are evolving to enable ultrascale versions of today’s applications and enable a new type of computational science, while remaining economically sustainable.

## Agenda

- Besides Architecture...
- Enabling Ultra-scale Applications
- Enabling New Computational Science
- Sustaining Economically

## Besides Hardware Architecture ...

- Efficient execution environment
- RAS
- OS architecture
- Linux scaled aggressively, with multiples in ultrascale configurations
- Robust scheduling
- RAS
- Packaging density / heat dissipation
- RAS

## Agenda

- Besides Architecture...
- Enabling Ultra-scale Applications
- Enabling New Computational Science
- Sustaining Economically

## Local Performance:Needed Flexibility of Memory Access

- Note: Original (Jan2003) models used for both X1 and Altix

**Absolute Performance**

## Ideal Machine (Technical/Economic Balance)

**Price Performance**

**Absolute Performance**

- High, cost-effective cache bandwidth of mass market parts
- Highest cost-effective memory bandwidth
- Design focus on gather/scatter

**Note:  For O(100KP) petascale machines, value of O(5X) processor performance advantage is less than today**

## Local Performance:  Multi-Paradigm

**Low                           ****Data locality****                            High**

**Low               ****Compute****               high**
**Intensity**

## Ultraviolet : Concept Architecture

**   ****UV Petascale GAM**
-     . Globally Addressable
-     . Low Latency
-     . High Bandwidth
-     . O(100K) Ports

**GPU**

**I/O**

**APU**

## Global Performance

- Communications
- grids becoming more dynamic -> low latency essential
- processor counts growing        -> low latency essential
- low latency -> global address space
- in clock periods, remote memory getting further away
- bandwidth-conserving operations needed
- high absolute link performance
- Synchronization
- current mechanisms insufficient for ultrascale
- optimizations will help, but maybe not enough
- new mechanisms needed
- Dynamic load balancing
- mechanisms need to mature, and interfaces become standard

## Challenges

- Clear virtual machine and performance models for these new mechanisms
- Compilers/tools that exploit these mechanisms mostly automatically and accept user hints
- Appropriate performance balance for typical uses
- Need to gain successful experience at very large scale (10-30KP) before going to ultrascale (100KP)

## Agenda

- Besides Architecture...
- Enabling Ultra-scale Applications
- Enabling New Computational Science
- Sustaining Economically

## Scientific Process

**Observe** existing data
- for patterns

**Hypothesize** models
- that match the data

**Test** those models
- to understand accuracy
- (i.e., add new data)

****Believed first coined by Scott Studham et al., PNNL**

## Scientific Process

**Observe** existing data
- for patterns

**Hypothesize** models
- that match the data

**Test** those models
- to understand accuracy
- (i.e., add new data)

****Believed first coined by Scott Studham et al., PNNL**

## Example:  Post-Genomic Biology

- <10% of the human genome is known to code for proteins
- Selective pressure generally removes unused genetic material
- What is the other 90% of the genome doing?
- Have the raw data (genome)
- Need to add other types of data (e.g., protein association info)
- Multi-petabytes of data all told
- Probably not a purely computational problem

## Differences from First Principles

- Data access patterns ~impossible to predict *a priori*            -> low latency / global address space
- New tools for data exploration needed
- need to automatically search for new, perhaps-vaguely-defined, patterns (that foster new theory)
- highly interactive/coupled with the scientist’s thought process
- but beware difficulty of launching new languages
- Contents of memory much more valuable
- RAS

## “and now for something completely different”:  Star-P

- Developed by Alan Edelman and colleagues at MIT, etc.
- Simple extensions to the MATLAB® language
- data parallel, MIMD, and mixed
- Builds on the existing base of MATLAB programs
- broadening the market for HPC systems
- New back-end server implemented for parallel execution
- Preserves key MATLAB strengths:
- very high level language
- interactivity / exploration
- easy visualization

**“****Put the fun back in supercomputing”**

## Agenda

- Besides Architecture...
- Enabling Ultra-scale Applications
- Enabling New Computational Science
- Sustaining Economically

## Key Points

- SGI retains system focus
- ...but uses commodity components wherever practical
- Exploit best mass-market processors (ItaniumTM)
- augment to make suitable for wider range of HPC apps
- Use Linux fully
- reap the cost benefits of reduced support of proprietary UnixTM variant
- IFB cables, EFI firmware
- Innovations for ultrascale must be relevant for wider markets
- e.g., multi-paradigm computing must accelerate ISV apps
- Use new technologies to broaden the market
- e.g., Star-P

- SGI’s systems are evolving to enable ultrascale versions of today’s applications and enable a new type of computational science, while remaining economically sustainable.

## One  step  ahead

**One  step  ahead**

- “There are no technology-independent lessons in computer science.”
- Butler Lampson, Xerox PARC