UPC and Titanium

UPC and Titanium

Open-source compilers and tools for scalable global address space computing

Kathy YelickUniversity of California, Berkeley and

Lawrence Berkeley National Laboratory

Center for Programming Models for Scalable Parallel Computing 2Review, March 13, 2003

Outline

• Global Address Languages in General

• UPC• Language overview• Berkeley UPC compiler status and microbenchmarks• Application benchmarks and plans

• Titanium• Language overview• Berkeley Titanium compiler status• Application benchmarks and plans


Global Address Space Languages• Explicitly-parallel programming model with SPMD parallelism

• Fixed at program start-up, typically 1 thread per processor

• Global address space model of memory• Allows programmer to directly represent distributed data structures

• Address space is logically partitioned• Local vs. remote memory (two-level hierarchy)

• Programmer control over performance critical decisions• Data layout and communication

• Performance transparency and tunability are goals• Initial implementation can use fine-grained shared memory

• Suitable for current and future architectures• Either shared memory or lightweight messaging is key

• Base languages differ: UPC (C), CAF (Fortran), Titanium (Java)


Global Address Space

• The languages share the global address space abstraction• Shared memory is partitioned by processors• Remote memory may stay remote: no automatic caching implied• One-sided communication through reads/writes of shared variables• Both individual and bulk memory copies

• Differ on details• Some models have a separate private memory area• Distributed arrays generality and how they are constructed

Shared

Glo

bal

ad

dre

ss

sp

ace

X[0]

Privateptr: ptr: ptr:

X[1] X[P]


UPC Programming Model Features

• SPMD parallelism• fixed number of images during execution• images operate asynchronously

• Several kinds of array distributions• double a[n] a private n-element array on each processor• shared double a[n] a n-element shared array, with cyclic mapping • shared [4] double a[n] a block cyclic array with 4-element blocks • shared [0] double *a = (shared [0] double *) upc_alloc(n);

a shared array with all elements local

• Pointers for irregular data structures• shared double *sp a pointer to shared data• double *lp a pointers to private data


UPC Programming Model Features

• Global synchronization• upc_barrier traditional barrier• upc_notify/upc_wait split-phase global synchronization

• Pair-wise synchronization• upc_lock/upc_unlock traditional locks

• Memory consistence has two types of accesses• Strict: must be performed immediately and atomically: typically

a blocking round-trip message if remote• Relaxed: still must preserve dependencies, but other

processors may view these as happening out of order

• Parallel I/O• Based on ideas in MPI I/O• Specification for UPC by Thakur, El Ghazawi et al


Berkeley UPC Compiler

UPC

Higher WHIRL

Lower WHIRL

• Compiler based on Open64• Recently merged Rice sources• Multiple front-ends, including gcc• Intermediate form called WHIRL

• Current focus on C backend• IA64 possible in future

• UPC Runtime • Pointer representation• Shared/distribute memory

• Communication in GASNet• Portable • Language-independent

Optimizingtransformations

C + Runtime

Assembly: IA64, MIPS,… + Runtime


Design for Portability & Performance• UPC to C translator:

• Translates UPC to C; insert runtime calls for parallel features

• UPC runtime: • Allocate shared data; implement pointers-to-shared

• GASNet: • A uniform interface for low-level communication primitives

• Portability: • C is our intermediate language

• GASNet is itself layered with a small core as the essential part

• High-Performance: • Native C compiler optimizes serial code

• Translator can perform communication optimizations

• GASNet can access network directly


Berkeley UPC Compiler Status• UPC Extensions added to front-end• Code-generation complete

• Some issues related to code quality (hints to backend compilers)

• GASNet communication layer• Running on Quadrics/Elan, IBM/LAPI, Myrinet/GM, and MPI• Optimized for small non-blocking messages and compiled code• Next step: strided and indexed put/get leveraging ARMCI work

• UPC Runtime layer • Developed and tested on all GASNet implementations• Supports multiple pointer representations• Next step: direct shared memory support

• Release scheduled for later this month• Glitch related to include files and usability to iron out


Pointer-to-Shared Representation

• UPC has three difference kinds of pointers:• Block-cyclic, cyclic, and indefinite (always local)

• A pointer needs a “phase” to keep track of where it is in a block• Source of overhead for updating and de-referencing• Consumes space in the pointer

• Our runtime has special cases for:• Phaseless (cyclic and indefinite) – skip phase update• Indefinite – skip thread id update

• Pointer size/representation easily reconfigured• 64 bits on small machines, 128 on large, word or struct

Address Thread Phase


Preliminary Performance• Testbed

• Compaq AlphaServer, with Quadrics GASNet conduit• Compaq C compiler for the translated C code

• Microbenchmarks• Measures the cost of UPC language features and construct• Shared pointer arithmetic, barrier, allocation, etc• Vector addition: no remote communication

• NAS Parallel Benchmarks • EP: no communication• IS: large bulk memory operations• MG: bulk memput • CG: fine-grained vs. bulk memput


Performance of Shared Pointer Arithmetic

• Phaseless pointers are an important optimization Indefinite pointers almost as fast as regular C pointers General blocked cyclic pointer 7x slower for addition

• Competitive with HP compiler, which generates native code Both compiler have known opportunities for improvement

Pointer-to-shared operations

05

101520253035404550

generic cyclic indefinite

type of pointer

# o

f c

yc

les

(1

.5 n

s/c

yc

le)

ptr + int -- HP

ptr + int -- Berkeley

ptr == ptr -- HP

ptr == ptr-- Berkeley


Cost of Shared Memory Access

Cost of Shared Local Memory Access

750

8

240

70

100

200

300

400

500

600

700

800

HP read Berkeleyread

HP write Berkeleywrite

# of

cyc

les

(1.5

ns/

cycl

e)

Cost of shared remote access

0

1000

2000

3000

4000

5000

6000

HP read Berkeleyread

HP write Berkeleywrite

# o

f cyc

les

(1.5

n

s/cy

cle)

• Local shared accesses somewhat slower than private ones HP has improved local performance in newer version

• Remote accesses worse than local, as expected Runtime/GASNet layering for portability is not a problem


NAS PB: EP

EP -- class A

0

10

20

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60

70

0 5 10 15 20

number of threads

Mo

ps

/se

co

nd HP

Berkeley

• EP = Embarrassingly Parallel has no communication• Serial performance via C code generation is not a problem


NAS PB: IS

• IS = Integer Sort is dominated by Bulk Communication• GASNet bulk communication adds no measurable overhead

IS -- Class B

0

5

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0 2 4 6 8 10

number of threads

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ps/

seco

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HP

Berkeley


NAS PB: MG

• MG = Multigrid involves medium bulk copies• “Berkeley” reveals a slight serial performance degradation due to casts• Berkeley-C uses the original C code for the inner loops

MG Class B

0100020003000400050006000700080009000

1 2 4 8 16 32Processors

Per

form

ance

Mfl

op/s Berkeley

Berkeley-C

F77+MPI


Scaling MG on the T3E

• Scalability of the language shown here for the T3E compiler• Directly shared memory support is probably needed to be competitive

on most current machines

MG Class C -- T3E

0

5000

10000

15000

20000

25000

0 50 100 150 200 250 300

Processors

Mo

ps/s UPC

MPI

linear


Mesh Generation in UPC

• Parallel Mesh Generation in UPC• 2D Delaunay triangulation

• Based on Triangle software by Shewchuk (UCB)

• Parallel version from NERSC uses dynamic load balancing, software caching, and parallel sorting


UPC Interactions• UPC consortium

• Tarek El-Ghazawi is coordinator: semi-annual meetings, ~daily e-mail• Revised UPC Language Specification (IDA,GWU,…)• UPC Collectives (MTU)• UPC I/O Specifications (GWU, ANL-PModels)

• Other Implementations• HP (Alpha cluster and C+MPI compiler (with MTU))• MTU (C+MPI Compiler based on HP compiler, memory model)• Cray (X1 implementation)• Intrepid (SGI implementation based on gcc)• Etnus (debugging)

• UPC Book: T. El-Ghazawi, B. Carlson, T. Sterling, K. Yelick• Goal is proofs by SC03

• HPC HPCS Effort• Recent interest from Sandia


Titanium

• Based on Java, a cleaner C++• classes, automatic memory management, etc.• compiled to C and then native binary (no JVM)

• Same parallelism model as UPC and CAF• SPMD with a global address space• Dynamic Java threads are not supported

• Optimizing compiler• static (compile-time) optimizer, not a JIT• communication and memory optimizations• synchronization analysis (e.g. static barrier analysis)• cache and other uniprocessor optimizations


Summary of Features Added to Java

1. Scalable parallelism (Java threads replaced)

2. Immutable (“value”) classes

3. Multidimensional arrays with iterators

4. Checked Synchronization

5. Operator overloading

6. Templates

7. Zone-based memory management (regions)

8. Libraries for collective communication, distributed arrays, bulk I/O


Immutable Classes in Titanium

• For small objects, would sometimes prefer• to avoid level of indirection • pass by value (copy entire object)• especially when immutable -- fields never modified

• Example: immutable class Complex { Complex () {real=0; imag=0; } ... } Complex c1 = new Complex(7.1, 4.3); c1 = c1.add(c1);

• Addresses performance and programmability• Similar to structs in C (not C++ classes) in terms of performance

• Adds support for complex types


Multidimensional Arrays

• Arrays in Java are objects

• Array bounds are checked

• Multidimensional arrays are arrays-of-arrays

Safe and general, but potentially slow

• New kind of multidimensional array added to Titanium• Sub-arrays are supported (interior, boundary, etc.)• Indexed by Points (tuple of ints)

• Combined with unordered iteration to enable optimizations foreach (p within A.domain()) {

A[p]... } • “A” could be multidimensional, an interior region, etc.


Communication

• Titanium has explicit global communication:• Broadcast, reduction, etc.• Primarily used to set up distributed data structures

• Most communication is implicit through the shared address space• Dereferencing a global reference, g.x, can generate

communication• Arrays have copy operations, which generate bulk communication:

A1.copy(A2) • Automatically computes the intersection of A1 and A2’s index

set or domain


Distributed Data Structures

• Building distributed arrays:

Particle [1d] single [1d] allParticle = new Particle [0:Ti.numProcs-1][1d]; Particle [1d] myParticle = new Particle [0:myParticleCount-1]; allParticle.exchange(myParticle);

• Now each processor has array of pointers, one to each processor’s chunk of particles

P0 P1 P2

All to all broadcast


Titanium Compiler Status

• Titanium compiler runs on almost any machine• Requires a C compiler (and decent C++ to compile translator)• Pthreads for shared memory• Communication layer for distributed memory (or hybrid)

• Recently moved to live on GASNet: obtained GM, Elan, and improved LAPI implementation

• Leverages other PModels work for maintenance

• Recent language extensions• Indexed array copy (scatter/gather style)• Non-blocking array copy under development

• Compiler optimizations• Cache optimizations, for loop optimizations• Communication optimizations for overlap, pipelining, and scatter/gather

under development


Applications in Titanium

• Several benchmarks• Fluid solvers with Adaptive Mesh Refinement (AMR)• Conjugate Gradient• 3D Multigrid• Unstructured mesh kernel: EM3D• Dense linear algebra: LU, MatMul• Tree-structured n-body code• Finite element benchmark• Genetics: micro-array selection• SciMark serial benchmarks

• Larger applications• Heart simulation• Ocean modeling with AMR (in progress)


Serial Performance (Pure Java)

Performance on a Pentium IV (1.5GHz)

050

100150200250300350400450

Overall FFT SOR MC Sparse LU

MF

lop

s

java C (gcc -O6) Ti Ti -nobc

• Several optimizations in Titanium compiler (tc) over the past year• These codes are all written in pure Java without performance extensions

SciMark Small - Linux, 1.8GHz Athlon, 256 KB L2, 1GB RAM

0

100

200

300

400

500

600

700

800

900

CompositeScore

FFT SOR Monte Carlo Sparse matmul LU

sunjdkibmjdktc1.1078tc2.87gcc


AMR for Ocean Modeling

• Ocean Modeling [Wen, Colella]• Require embedded boundaries to model the ocean floor and

coastline• Results in irregular data structures and array accesses

• Starting with AMR solver this year for ocean flow• Compiler and language support for irregular problem under

design

Graphics from Titanium AMR Gas Dynamics [McCorquodale,Colella


• Immersed Boundary Method [Peskin/MacQueen]• Fibers (e.g., heart muscles)

modeled by list of fiber points

• Fluid space modeled by a regular lattice

• Irregular fiber lists need to interact with regular fluid lattice• Trade-off between load balancing

of fibers and minimizing communication

• Memory and communication intensive

Heart Simulation

• Random array access is key problem in the performance• Developed compiler optimizations to improve their performance

• Application effort funded by NSF/NPACI


Parallel Performance and Scalability• Poisson solver using “Method of Local Corrections” [Balls, Colella]

• Communication < 5%; Scaled speedup nearly ideal (flat)

IBM SP Cray T3E


Titanium Interactions

• GASNet interactions• In addition to the

• Application collaborators• Charles Peskin and Dave McQueen and Courant Institute• Phil Colella and Tong Wen and LBNL• Scott Baden and Greg Balls and UCSD

• Involved in Sun HPCS Effort

• The GASNet work is common to UPC and Titanium• Joint effort between U.C. Berkeley and LBNL• (UPC project is primarily at LBNL; Titanium is U.C. Berkeley)• Collaboration with Nieplocha on communication runtime

• Participation in Global Address Space tutorials


The End

•http://upc.nersc.gov•http://titanium.cs.berkeley.edu/


NAS PB: CG

• CG = Conjugate Gradient can be written naturally with fine-grained communication in the sparse matrix-vector product• Worked well on the T3E (and hopefully will on the X1)• For other machines, a bulk version is required

NAS CG Performance

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2 4 8

Pe

rfo

rma

nce

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ps/s

ec

compaq

berkeley2

berkeley3

f77+MPI

Documents

UPC and Titanium