Transcript Document

Experiences with Co-array Fortran on
Hardware Shared Memory Platforms
Yuri Dotsenko
John Mellor-Crummey
Cristian Coarfa
Daniel Chavarria-Miranda
Rice University, Houston, TX
Co-array Fortran
Global Address Space (GAS) language
SPMD programming model
Simple extension of Fortran 90
Explicit control over data placement and
computation distribution
Private data
Shared data: both local and remote
One-sided communication (PUT and GET)
Team and point-to-point synchronization
Co-array Fortran: Example
integer :: a(10,20)[*]
a(10,20)
a(10,20)
a(10,20)
image 1
image 2
image N
if (this_image() > 1)
Copies from left neighbor
a(1:10,1:2) = a(1:10,19:20)[this_image()-1]
image 1
image 2
image N
Compiling CAF
Source-to-source translation
Prototype Rice cafc
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Fortran 90 pointer-based co-array representation
ARMCI-based data movement
Goal: performance transparency
Challenges:
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Retain CAF source-level information
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Array contiguity, array bounds, lack of aliasing
Exploit efficient fine-grain communication on SMPs
Outline
Co-array representation and data access
Local data
 Remote data
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Experimental evaluation
Conclusions
Representation and Access for
Local Data
Efficient local access to SAVE/COMMON coarrays is crucial to achieving best
performance on a target architecture
Fortran 90 pointer
Fortran 90 pointer to structure
Cray pointer
Subroutine argument
COMMON block (need support for symmetric
shared objects)
Fortran 90 Pointer Representation
CAF declaration:
real, save :: a(10,20)[*]
After translation:
type T1
integer(PtrSize) handle
real, pointer :: local(:,:)
end type T1
type (T1) ca
Local access:
ca%local(2,3)
Portable representation
Back-end compiler has no knowledge about:
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Potential aliasing (no-alias flags for some compilers)
Contiguity
Bounds
Implemented in cafc
Fortran 90 Pointer to Structure
Representation
CAF declaration:
real, save :: a(10,20)[*]
After translation:
type T1
real :: local(10,20)
end type T1
type (T1), pointer :: ca
Conveys constant bounds and contiguity
Potential aliasing is still a problem
Cray Pointer Representation
CAF declaration:
real, save :: a(10,20)[*]
After translation:
real :: a_local(10,20)
pointer (a_ptr, a_local)
Conveys constant bounds and contiguity
Potential aliasing is still a problem
Cray pointer is not in Fortran 90 standard
Subroutine Argument
Representation
CAF source:
subroutine foo(…)
real, save :: a(10,20)[*]
a(i,j) = … + a(i-1,j) * …
end subroutine foo
After translation:
subroutine foo(…)
! F90 representation for co-array a
call foo_body(ca%local(1,1), ca%handle, …)
end subroutine foo
subroutine foo_body(a_local, a_handle, …)
real :: a_local(10,20)
a_local(i,j) = … + a_local(i-1,j) * …
end subroutine foo_body
Subroutine Argument
Representation (cont.)
Avoid conservative assumptions about coarray aliasing by the back-end compiler
Performance is close to optimal
Extra procedures and procedure calls
Implemented in cafc
COMMON Block Representation
CAF declaration:
real :: a(10,20)[*]
common /a_cb/ a
After translation:
real :: ca(10,20)
common /ca_cb/ ca
Yields best performance for local accesses
OS must support symmetric data objects
Outline
Co-array representation and data access
Local data
 Remote data
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Experimental evaluation
Conclusions
Generating CAF Communication
Generic parallel architectures
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Library function calls to move data
Shared memory architectures (load/store)
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Fortran 90 pointers
Vector of Fortran 90 pointers
Cray pointers
Communication Generation for
Generic Parallel Architectures
CAF code:
a(:) = b(:)[p] + …
Translated code:
allocate b_temp(:)
call GET( b, p, b_temp, … )
a(:) = b_temp(:) + …
deallocate b_temp
Portable: works on clusters and SMPs
Function overhead per fine-grain access
Uses temporary to hold off-processor data
Implemented in cafc
Communication Generation
Using Fortran 90 Pointers
CAF code:
do j = 1, N
C(j) = A(j)[p]
end do
Translated code:
do j =
ptrA
call
C(j)
end do
1, N
=> A(j)
CafSetPtr(ptrA,p,A_handle)
= ptrA
Function call overhead for each reference
Implemented in cafc
Pointer Initialization Hoisting
Naïvely translated code:
do j =
ptrA
call
C(j)
end do
1, N
=> A(j)
CafSetPtr(ptrA,p,A_handle)
= ptrA
Code with hoisted pointer initialization:
ptrA => A(1:N)
call CafSetPtr(ptrA,p,A_handle)
do j = 1, N
C(j) = ptrA(j)
end do
Pointer initialization hoisting is not yet implemented in cafc
Communication Generation Using
Vector of Fortran 90 Pointers
CAF code:
do j = 1, N
C(j) = A(j)[p]
end do
Translated code:
… initialization …
do j = 1, N
C(j) = ptrVectorA(p)%ptrA(j)
end do
Does not require pointer initialization hoisting
and avoids function calls
Worse performance than that of hoisted
pointer initialization
Communication Generation
Using Cray Pointers
CAF code:
do j = 1, N
C(j) = A(j)[p]
end do
Translated code:
integer(PtrSize) :: addrA(:)
… addrA initialization …
do j = 1, N
ptrA = addrA(p)
C(j) = A_rem(j)
end do
addrA(p) – address of co-array A on image p
Cray pointer initialization hoisting yields only marginal
improvement
Outline
Co-array representation and data access
Local data
 Remote data
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Experimental evaluation
Conclusions
Experimental Platforms
SGI Altix 3000
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128 Itanium2 1.5 GHz, 6 MB L3 cache processors
Linux (2.4.21 kernel)
Intel Fortran Compiler 8.0
SGI Origin 2000
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16 MIPS R12000 350 MHz, 8 MB L2 cache processors
IRIX64 6.5
MIPSpro Compiler 7.3.1.3m
Benchmarks
STREAM
Random Access
Spark98
NAS MG and SP
STREAM
DO J = 1, N
C(J) = A(J)
END DO
Copy kernel
DO J = 1, N
C(J) = A(J)[p]
END DO
DO J = 1, N
A(J)=B(J)+s*C(J)
END DO
Triad kernel
DO J = 1, N
A(J)=B(J)[p]+s*C(J)[p]
END DO
Goal: investigate how well architecture bandwidth can be delivered
up to the language level
STREAM: Local Accesses
COMMON block is the best, if platform allows
Subroutine parameter has similar performance to
COMMON block representation
Pointer-based representations have performance within
5% of the best on the Altix (with no-aliasing flag), and
within 15% on the Origin
Fortran 90 pointer representation yields 30% of
performance on the Altix without using the flag to specify
lack of pointer aliasing
Array section statements with Fortran 90 pointer
representation yield 40-50% performance on the Origin
STREAM: Remote Accesses
COMMON block representation for local access + Cray pointer
for remote accesses is the best
Subroutine argument + Cray pointer for remote accesses has
similar performance
Remote accesses with function call per access yield very poor
performance (24 times slower than the best on the Altix, five
times slower on the Origin)
Generic strategy (with intermediate temporaries) delivers only
50-60% of performance on the Altix and 30-40% of performance
on the Origin for vectorized code (except for Copy kernel)
Pointer initialization hoisting is crucial for Fortran 90 pointers
remote accesses and desirable for Cray pointers
Similarly coded OpenMP version has comparable performance
on the Altix (90% for the scale kernel) and 86-90% on the Origin
Spark98
Based on CMU’s earthquake simulation code
Computes sparse matrix-vector product
Irregular application with fine-grain accesses
Matrix distribution and computation partitioning
is done offline (sf2 traces)
Spark98 computes partial product locally, then
assembles the result across processors
Spark98 (cont.)
Versions
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Serial (Fortran kernel, ported from C)
MPI (Fortran kernel, ported from C)
Hybrid (best shared memory threaded version)
CAF versions (based on MPI version):
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CAF Packed PUTs
CAF Packed GETs
CAF GETs (computation with remote data accessed “in
place”)
Spark98 GETs Result Assembly
v2(:,:) = v(:,:)
call sync_all()
do s = 0, subdomains-1
if (commindex(s) < commindex(s+1)) then
pos = commindex(s)
comm_len = commindex(s+1) - pos
v(:, comm(pos:pos+comm_len-1)) =
&
v(:, comm(pos:pos+comm_len-1)) +
&
v2(:, comm_gets(pos:pos+comm_len-1))[s]
end if
end do
call sync_all()
Spark98 GETs Result Assembly
v2(:,:) = v(:,:)
call sync_all()
do s = 0, subdomains-1
if (commindex(s) < commindex(s+1)) then
pos = commindex(s)
comm_len = commindex(s+1) - pos
v(:, comm(pos:pos+comm_len-1)) =
&
v(:, comm(pos:pos+comm_len-1)) +
&
v2(:, comm_gets(pos:pos+comm_len-1))[s]
end if
end do
call sync_all()
Spark98 Performance on Altix
Performance of all CAF versions is comparable to
that of MPI and better on large number of CPUs
CAF GETs is simple and more “natural” to code,
but up to 13% slower
Without considering locality, applications do not
scale on NUMA architectures (Hybrid)
ARMCI library is more efficient than MPI
NAS MG and SP
Versions:
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MPI (NPB 2.3)
CAF (based on MPI NPB 2.3)
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Generic code generation with subroutine argument coarray representation (procedure splitting)
Shared memory code generation (Fortran 90 pointers;
vectorized source code) with subroutine argument coarray representation
OpenMP (NPB 3.0)
Class C
NAS SP Performance on Altix
Performance of CAF versions is comparable to that of MPI
CAF-generic has better performance than CAF-shm
because it uses memcpy, which hides latency by
keeping optimal number of memory ops in flight
OpenMP scales poorly
NAS MG Performance on Altix
Conclusions
Direct load/store communication improves
performance of fine-grain accesses by a factor
of 24 on the Altix 3000 and five on the Origin
2000
“In-place” data use in CAF statements incurs
acceptable abstraction overhead
Performance comparable to that of MPI codes
for fine- and coarse-grain applications
We plan to implement in cafc optimal,
architecture dependent, code generation for
local and remote co-array accesses
www.hipersoft.rice.edu/caf