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HPC User Forum 2012 Panel on Potential Disruptive Technologies Emerging Parallel Programming Approaches. Guang R. Gao Founder ET International Inc Newark, Delaware USA [email protected] Who is ETI ?. From “Cool Vendors” Report – By Gartner ( April 17,2012 ): [

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Guang r gao founder et international inc newark delaware usa ggao etinternational

HPC User Forum 2012 Panel on Potential Disruptive TechnologiesEmerging Parallel Programming Approaches

Guang R. Gao

Founder

ET International Inc

Newark, Delaware

USA

[email protected]


Guang r gao founder et international inc newark delaware usa ggao etinternational

Who is ETI ?

From “Cool Vendors” Report –

By Gartner (April 17,2012):

[

ET International

Newark, Delaware (www.etinternational.com)

Analysis by Carl Claunch

Why Cool:

ET International delivers its dataflow-oriented ETI Swarm environment for garnering high efficiency from highly parallel software, based on the alternative ParalleX execution model. As highly parallel execution becomes essential to addressing the more substantial computing tasks that HPC users face today, progress is increasingly being stymied by the application's inability to keep all the parallel strands working productively.

…]


Guang r gao founder et international inc newark delaware usa ggao etinternational

Motivation

  • Many-core is coming

    • Current paradigms don't have the expressive power to harness concurrency

  • Hardware is getting more heterogeneous

    • Current hybrid programming techniques (OpenMP+MPI+OpenCL) are not maintainable: too complicated

  • Caches are disappearing or becoming non-coherent

    • Distributed memory is everywhere, and at different levels

  • Fine grained power management

    • Use what you need and turn off/down the rest

  • Failure is the norm

    • Resilience must be baked in the whole stack (application, compiler, runtime, hardware)

  • Increasing Application Computation/data Irregularity

    • Static scheduling can no longer properly load balance


Eti vision

ETI Vision

  • We need new “Execution Models”!

  • Leverage ETI’s deep and growing IP position based on 25+ years of applied R&D expertise and $20M+ in R&D software engineering and development

    • (e.g. extensive system software base for Cyclops, CELL, SCC, Intel Runnemede, Intel X86 based machines, Adapteva, etc)

  • Provide high-performance SWARM software solutions to our OEM’s, partners and direct customers

  • Advance SWARM solutions to address optimization opportunities driven by heterogeneous multi-/many- core processing including:

    • Big Compute (Private HPC Cloud)systems

    • Big Data HPC systems

    • HPC embedded appliances

    • etc


Execution paradigm comparisons

Execution Paradigm Comparisons

MPI, OpenMP, OpenCL

SWARM

Time

Time

Active threads

Waiting

  • Asynchronous Event-Driven Tasks

  • Dependencies

  • Resources

  • Active Messages

  • Control Migration

  • Communicating Sequential Processes

  • Bulk Synchronous

  • Message Passing


Swarm execution overview

SWARM Execution Overview

Enabled Tasks

Tasks with Unsatisfied Dependencies

Tasks enabled

SWARM

Dependencies

satisfied

Tasks mapped to resources

Resources in Use

CPU

CPU

CPU

CPU

Available Resources

CPU

CPU

CPU

GPU

CPU

Resources allocated

CPU

CPU

CPU

GPU

GPU

Resources released


Case studies of fine gran execution models

FT-06-09-2011-Gao

Case Studies of Fine-Gran Execution Models

  • Static Dataflow Model (1970s - )

  • EARTH Model (1988 - )

  • TNT Model and Cyclops-64 (2003 - )

  • Codelet Model under

    Intel-led DARPA/UHPC


Guang r gao founder et international inc newark delaware usa ggao etinternational

DARPA/Intel Runnemede Program

ET International, Inc.

1000X Energy reduction

Heterogeneous, Tightly-Coupled

Simple Architecture

System Management

& Concurrency

Assured Operation

Event driven codelets

Self-aware introspection

Code and data motion

CPU

<10% overhead

Checkpoint with Flash/CPM

Security Through Sandboxing

Resiliency

Execution

Model

HW/SW

Co-Design

University of Illinois

Interconnect

Fabric

Productivity

Application Efficiency

Data Movement

Model-based

Goal-oriented

Self-morphing

Heterogeneous & tapered

Large local memory

Memory

Courtesy of The Intel

DARPA UHPC Team

1000X energy reduction

Overhauled DRAM mArch

Resilient memory

Our Collaborators


Progress proof points to date

Progress & Proof Points To-Date


Barnes hut swarm vs openmp

Barnes-HutSWARM vsOpenMP

Barnes-Hut SWARM vs OpenMP

Ideal

SWARM

OpenMP

Barnes-Hut


Swarm mpi performance comparison

SWARM/MPI Performance Comparison

Consistent Speed-up from 2X to 14.5X


Cholesky decomposition swarm vs mkl scalapack

Cholesky Decomposition (SWARM vs MKL/ScaLAPACK)

Cholesky Decomposition (SWARM vs MKL/ScaLAPACK


Summary and acknowledgements

Summary and Acknowledgements

  • Summary (productivity observation)

    • N-Body: 1 man-day, 3X

    • G-500: 1 man-month, upto 14x

    • Cholesky: 2 man-week, 1.5x

      NOTE: the base is performance of optimized code

  • Acknowledgements

    • Our Sponsors

    • Our Collaborators and Colleagues

    • My Host

    • Others

.


Cholesky profiles

Cholesky Profiles

SWARM

OpenMP


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