PT-4142, Porting and Optimizing OpenMP applications to APU using CAPS tools, by Jean-Charles Vasnier

PORTING AND OPTIMIZING OPENMP APPLICATIONS TO APU USING CAPS TOOLS

JEAN-‐CHARLES VASNIER, CAPS ENTREPRISE

2 | PRESENTATION TITLE | NOVEMBRE 19, 2013 | CONFIDENTIAL

AGENDA

y  CAPS enterprise y  OpenACC y  CAPS Compilers

y  CAPS OpenMP Compiler for AMD APUs ‒ Compiler analyzes and code generaPon ‒  InteracPve report

y  ExperimentaPons with benchmark applicaPons ‒ HydroC

y  Future work

CAPS OpenMP Compiler - June 2013 2

CAPS enterprise


y  Founded in 2002 ‒  Large experPse in processor micro-‐architecture and code generaPon ‒  Spin-‐off of French INRIA Research Lab ‒ 30 employees

y  Mission: to help its customers to leverage the performance of mulP/manycore machines ‒ ConsulPng & engineering services ‒ CAPS OpenACC Compiler & toolchain ‒ Trainings

y  Expanding sales worldwide ‒ Resellers in US and APAC (Exxact, Abso^, JCC Gimmick Ltd, Nodasys, …)

www.caps-entreprise.com 4

COMPANY PROFILE

5 | PRESENTATION TITLE | NOVEMBRE 19, 2013 | CONFIDENTIAL www.caps-entreprise.com 5

CAPS ECOSYSTEM

Business Partners

European R&D Projects

Customers

OpenACC


OPENACC INITIATIVE

y  A CAPS, CRAY, Nvidia and PGI initiative

y  Open Standard

y  A directive-based approach for programming heterogeneous many-core hardware for C and FORTRAN applications

y  http://www.openacc-standard.com



DIRECTIVE-‐BASED PROGRAMMING (1)

y  Three ways of programming GPGPU applications:


Libraries

Ready-to-use Acceleration

Directives

Quickly Accelerate Existing Applications

Programming Languages

Maximum Performance


DIRECTIVE-‐BASED PROGRAMMING (2)

www.caps-‐entreprise.com 9


EXECUTION MODEL

y  Among a bulk of computations executed by the CPU, some regions can be offloaded to hardware accelerators ‒ Parallel regions ‒ Kernels regions

y  Host is responsible for: ‒ Allocating memory space on accelerator ‒  Initiating data transfers ‒ Launching computations ‒ Waiting for completion ‒ Deallocating memory space

y  Accelerators execute parallel regions: ‒ Use work-sharing directives ‒ Specify level of parallelization



OPENACC EXECUTION MODEL

y  Host-‐controlled execuPon y  Based on three parallelism levels

‒ Gangs – coarse grain ‒ Workers – fine grain ‒ Vectors – finest grain


Device

Gang

Worker

Vectors

Gang

Worker

Vectors

…

CAPS Compilers


OPENACC COMPILERS (1)

CAPS Compilers: y Source-‐to-‐source compilers y Support Intel Xeon Phi, NVIDIA GPUs,

AMD GPUs and APUs

PGI Accelerator y Extension of x86 PGI compiler y Support Intel Xeon Phi, NVIDIA GPUs,

AMD GPUs and APUs


Cray Compilers: y Provided with Cray system only


CAPS COMPILERS (2)

Are source-to-source compilers, composed of 3 parts:

y The directives (OpenACC or OpenHMPP) ‒ Define parts of code to be accelerated ‒ Indicate resource allocation and communication ‒ Ensure portability

y The toolchain ‒ Helps building manycore applications ‒ Includes compilers and target code generators ‒ Insulates hardware specific computations ‒ Uses hardware vendor SDK

y The runtime ‒ Helps to adapt to platform configuration ‒ Manages hardware resource availability



CAPS COMPILERS (3)

y Take the original applicaPon as input and generate another applicaPon source code as output ‒ AutomaPcally turn the OpenACC source code into a accelerator-‐specific source code (CUDA, OpenCL)

y Compile the enPre hybrid applicaPon y  Just prefix the original compilaPon line with capsmc to produce a hybrid applicaPon

y CompaPble with: ‒ GNU ‒ Intel ‒ Open64 ‒ Abso^ ‒ …


$ capsmc gcc myprogram.c $ capsmc gfortran myprogram.f90


CAPS COMPILERS (4)

y CAPS Compilers drives all compilation passes

y Host application compilation ‒ Calls traditional CPU compilers ‒ CAPS Runtime is linked to the host part of the application

y Device code production ‒ According to the specified target

‒ A dynamic library is built


Fun #3

C++ Frontend

C Frontend

Fortran Frontend

CUDA Code GeneraPon

Executable (mybin.exe)

Instrumen-‐taPon module

CPU compiler (gcc, ifort, …) CUDA compilers

HWA Code (Dynamic library)

OpenCL GeneraPon

OpenCL compilers

ExtracPon module

Fun #2

Host code

codelets

CAPS RunDme

Fun #1

From OpenMP To OpenACC


CAPS OPENMP COMPILER

y  AutomaPcally turns OpenMP codes into OpenACC

y  Diagnoses compaPbility issues and suggests code transformaPons

y  Builds accelerated versions based on CUDA or OpenCL y  Works with all plalorms

‒ AMD and Nvidia GPUs ‒ AMD APUs ‒  Intel Xeon Phi



CAPS OPENMP COMPILER OVERVIEW

Profiling Analysis AcceleraPon



y  Converts OpenMP codes into OpenACC ‒ Examine OpenMP loop nests and check their OpenACC compaPbility ‒ Diagnose non compaPbility issues and propose advice ‒ Build an APU version based on OpenCL

y  Builds a interacPve report ‒ Based on the compiler staPc and dynamic analyses ‒ OpenMP to OpenACC kernels view o Performance details of each region ‒ Regions’ In/Out and data dependencies between regions ‒ Gives the user control on pushing kernels onto GPU and manage data transfers

EXTENSION OF THE CAPS OPENACC COMPILER


OPENMP-‐BASED OPTIMIZATION PROCESS


Execution

Profiling report

Generation

Accelerated executable

Application with OpenMP

directives

Instrumentation

Tracable application

Analysis

HTML interactive

report


y  Code preprocessing and instrumentaPon ‒  IdenPfy supported OpenMP regions

‒  parallel, parallel for and parallel for constructs ‒  Instrument the code to track data and measure kernel performance

y  Instrumented applicaPon execuPon ‒ Based on the user data set ‒ Number of Pmes a OpenMP region is executed ‒ Region’s reads and writes ‒ Range of loops iteraPon ‒ Region performance

INSTRUMENTATION AND PROFILING PHASES


y  Generates an interacPve HTML report ‒ Based on the compiler staPc and dynamic analyses ‒ Metrics for each OpenMP regions

‒  Check OpenACC compliancy ‒  ComputaPon density ‒  Coalescing of data accesses ‒  EsPmated speed-‐up ‒ Memory usage

‒ Propose a GPU execuPon or naPve OpenMP execuPon ‒ Data usage and data dependencies graph between regions

‒ Determine when transfers are required between kernels ‒  Let the user modify the CPU or GPU execuPon and data transfer policy

ANALYSIS PHASE


HTML INTERACTIVE REPORT (1)

y  Get regions overview in a snap!

y  Code View: from OpenMP to OpenACC direcPves




y Performance details of each region

y Analysis conclusions and portability diagnosis

CAPS OpenMP Compiler -‐ June 2013 25



y  Regions’ inputs/outputs and data dependencies map




y  Get the control! ‒ Manually push kernels onto accelerators ‒ Manage data transfers



y  Same as the CAPS OpenACC Compiler ‒ Based on the analysis report ‒ Generates OpenCL kernels from OpenACC ‒ AutomaPc data updates to ensure memory coherency

CODE GENERATION PHASE


FEATURES

y  Diagnoses ‒ OpenACC compliancy ‒ ComputaPonal density ‒ Data accesses coalescing ‒ Memory usage ‒ EsPmated speed-‐up

y  AutomaPc porPng to AMD, NVIDIA, or Intel accelerators

y  Accelerates execuPon or keeps the OpenMP naPve one

y  Gives users control to manual opPmizaPons


ApplicaPon ExperimentaPons


HARDWARE AND SOFTWARE ENVIRONMENT

y  Linux system ‒ AMD SDK 2.8 ‒ CAPS Compiler revision 50387 ‒ GCC 4.6.1 ‒ OpenMPI 1.6.4

y  Hardware ‒ AMD A10-‐5800K APU with Radeon HD Graphics



APPLICATIONS STATUS

y  Main objecPve is proof of concept, not performance ‒ Performance limitaPons of current version of the APU

y  HydroC ‒ Most convincing demo ‒  x1.3 speed-‐up by modifying the execuPon and transfer policy



HYDROC HTML REPORT

Fututre Work C2PO


C2PO MISSION STATEMENT

y  Combines various CAPS technologies in a modular tool chain ‒  StaPc and dynamic code analyzers ‒ OpenMP to OpenACC code transformers ‒ Kernel micro-‐bencher ‒ Plug with third-‐party tools: Vtune, CUDA profiler ‒ Use CAPS Compiler at final stage to produce manycore applicaPon

C2PO - Oct. 2013 35

Guides you through the whole process of porPng and tuning applicaPons onto manycore parallel systems


C2PO PHASES

1.  GeneraPon of an OpenACC skeleton from OpenMP or sequenPal code ‒  Hotspot detecPon and dataflow analysis

2.  Indicates global and local advice on ‒ Data management/placement between kernels or regions ‒  First ten Pps on kernel performance

‒  Data coalescing, parallelism, gridificaPon, loops order

3.  Let you rapidly opPmize performance of kernels ‒  Extracts funcPons, loops or annotated regions ‒  Tune kernel code following C2PO advice ‒  Replay standalone with applicaPon data and measure performance gain ‒  Re-‐inject opPmized into applicaPon source code

4.  Use CAPS Compilers to build Intel Xeon Phi, NVIDIA or AMD GPUs

C2PO - Oct. 2013 36

Dataflow analysis

OpenACC skeleton generaPon

Extract loops, funcPons, regions

Fine tune kernels

User Input


C2PO TOOL CHAIN

C2PO - Oct. 2013 37

OpenACC Generator

Data Movement Analyzer SequenPal

Code

OpenMP Code

HTML Report

OpenACC Code

ubencher

InteracPve Report

Kernels Performance analyzer

Code skeleton generaDon

Global tuning

Local tuning

CUDA profiler

VTune


C2PO OPENACC GENERATION

y  From sequenPal or OpenMP code to first parallelized code ‒  Instrument applicaPon and detect hotspots ‒ Generate OpenACC skeleton of kernels from loops ‒ Manage data transfers between kernels

y  A report is generated containing ‒ Various performance metrics

‒  Kernel execuPon ‒ Memory reads and writes ‒  PotenPal performance gain

‒ Data dependencies and usage between kernels ‒ OpenACC code view

C2PO - Oct. 2013 38


C2PO GLOBAL TUNING

y  Dynamic tracking of data so as to opPmize their movement ‒ Dynamically trace uploads and downloads at execuPon Pme ‒ Detect potenPally redundant data transfers

C2PO - Oct. 2013 39

#openacc data region // convergence loop for { Upload data() Kernels’ calls() Download data() } …

Difficult for the compiler to detect any CPU use of data

Possible advice: are the following parameters modified

by the CPU between the downloads and uploads?

If yes, insert OpenACC data region with non modified parameters


C2PO TUNING PHASE

y  Microbenchmarking mechanism ‒  Loops, funcPons, user annotated regions are extracted in kernels ‒ Apply opPmizaPons ‒ Replay kernels with original data set without running the whole applicaPon ‒ Once tuned, inject kernels into the applicaPon source code

y  Apply performance analyzers from third party tools (Vtune, CUDA profiler) ‒  Synthesizes raw metrics (hardware counters) linked to the source code

C2PO - Oct. 2013 40


C2PO OBJECTIVES AND BENEFITS

y  Keep one single OpenMP code for various parallel many-‐core systems (GPUs, APUs, MIC)

y  Incrementally port and opPmize codes in a modular way

y  Use an interacPve compiler: advice from dynamic and staPc analyses at source code level

C2PO - Oct. 2013 41

THANK YOU FOR YOUR ATTENTION!

Vasnier Jean-‐Charles Sales Engineer, CAPS entreprise

Phone: +1-‐865-‐227-‐6899 Email: jvasnier@caps-‐entreprise.com


GET PERFORMANCE IN NO TIME!


‒  Measured on a dual Sandy bridge E5-‐2687W with 32 Go RAM and a Kepler K20C driven by CUDA v5.0

45,698

63,42

27,539

12,71

23,417

12,55

0

10

20

30

40

50

60

70

Hydro Nbody

ExecuD

on Tim

e (secon

ds)

Original (OpenMP)

Generated (auto)

Generated(tweaked)

x2 speed-‐up (a^er user’s tuning)

x6 speed-‐up in 3 clicks (full automaPc)

Date post:	10-May-2015
Category:	Technology
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PT-4142, Porting and Optimizing OpenMP applications to APU using CAPS tools, by Jean-Charles Vasnier

Technology