automatic performance tuning of sparse-matrix-vector-multiplication (spmv) and iterative sparse...
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Automatic Performance Tuningof
Sparse-Matrix-Vector-Multiplication (SpMV)
andIterative Sparse Solvers
James Demmel
www.cs.berkeley.edu/~demmel/cs267_Spr09
Outline
• Motivation for Automatic Performance Tuning• Results for sparse matrix kernels
– Sparse Matrix Vector Multiplication (SpMV)– Sequential and Multicore results
• OSKI = Optimized Sparse Kernel Interface• Tuning Entire Sparse Solvers
Berkeley Benchmarking and OPtimization (BeBOP)
• Prof. Katherine Yelick• Current members
– Kaushik Datta, Mark Hoemmen, Marghoob Mohiyuddin, Shoaib Kamil, Rajesh Nishtala, Vasily Volkov, Sam Williams, …
• Previous members– Hormozd Gahvari, Eun-Jim Im, Ankit Jain, Rich Vuduc,
many undergrads, …
• Many results here from current, previous students• bebop.cs.berkeley.edu
Automatic Performance Tuning
• Goal: Let machine do hard work of writing fast code• What does tuning of dense BLAS, FFTs, signal
processing, have in common?– Can do the tuning off-line: once per architecture,
algorithm– Can take as much time as necessary (hours, a week…)– At run-time, algorithm choice may depend only on few
parameters (matrix dimensions, size of FFT, etc.)
• Can’t always do tuning off-line– Algorithm and implementation may strongly depend on
data only known at run-time– Ex: Sparse matrix nonzero pattern determines both best
data structure and implementation of Sparse-matrix-vector-multiplication (SpMV)
– Part of search for best algorithm just be done (very quickly!) at run-time
Source: Accelerator Cavity Design Problem (Ko via Husbands)
Linear Programming Matrix
…
A Sparse Matrix You Encounter Every Day
Matrix-vector multiply kernel: y(i) y(i) + A(i,j)*x(j)
for each row i
for k=ptr[i] to ptr[i+1] do
y[i] = y[i] + val[k]*x[ind[k]]
SpMV with Compressed Sparse Row (CSR) Storage
Matrix-vector multiply kernel: y(i) y(i) + A(i,j)*x(j)
for each row i
for k=ptr[i] to ptr[i+1] do
y[i] = y[i] + val[k]*x[ind[k]]
Only 2 flops per 2 mem_refs:Limited by getting data from memory
Example: The Difficulty of Tuning
• n = 21200• nnz = 1.5 M• kernel: SpMV
• Source: NASA structural analysis problem
Example: The Difficulty of Tuning
• n = 21200• nnz = 1.5 M• kernel: SpMV
• Source: NASA structural analysis problem
• 8x8 dense substructure: exploit this to limit #mem_refs
Speedups on Itanium 2: The Need for Search
Reference
Best: 4x2
Mflop/s
Mflop/s
Register Profile: Itanium 2
190 Mflop/s
1190 Mflop/s
Register Profiles: IBM and Intel IA-64
Power3 - 17% Power4 - 16%
Itanium 2 - 33%Itanium 1 - 8%
252 Mflop/s
122 Mflop/s
820 Mflop/s
459 Mflop/s
247 Mflop/s
107 Mflop/s
1.2 Gflop/s
190 Mflop/s
Register Profiles: Sun and Intel x86
Ultra 2i - 11% Ultra 3 - 5%
Pentium III-M - 15%Pentium III - 21%
72 Mflop/s
35 Mflop/s
90 Mflop/s
50 Mflop/s
108 Mflop/s
42 Mflop/s
122 Mflop/s
58 Mflop/s
Another example of tuning challenges
• More complicated non-zero structure in general
• N = 16614• NNZ = 1.1M
Zoom in to top corner
• More complicated non-zero structure in general
• N = 16614• NNZ = 1.1M
3x3 blocks look natural, but…
• More complicated non-zero structure in general
• Example: 3x3 blocking– Logical grid of 3x3 cells
• But would lead to lots of “fill-in”
Extra Work Can Improve Efficiency!
• More complicated non-zero structure in general
• Example: 3x3 blocking– Logical grid of 3x3 cells– Fill-in explicit zeros– Unroll 3x3 block multiplies– “Fill ratio” = 1.5
• On Pentium III: 1.5x speedup!– Actual mflop rate 1.52 =
2.25 higher
Automatic Register Block Size Selection
• Selecting the r x c block size– Off-line benchmark
• Precompute Mflops(r,c) using dense A for each r x c
• Once per machine/architecture– Run-time “search”
• Sample A to estimate Fill(r,c) for each r x c– Run-time heuristic model
• Choose r, c to minimize time ~ Fill(r,c) / Mflops(r,c)
Accurate and Efficient Adaptive Fill Estimation• Idea: Sample matrix
– Fraction of matrix to sample: s [0,1]– Control cost = O(s * nnz ) by controlling s
• Search at run-time: the constant matters!• Control s automatically by computing statistical
confidence intervals, by monitoring variance• Cost of tuning
– Lower bound: convert matrix in 5 to 40 unblocked SpMVs
– Heuristic: 1 to 11 SpMVs• Tuning only useful when we do many SpMVs
– Common case, eg in sparse solvers
Accuracy of the Tuning Heuristics (1/4)
NOTE: “Fair” flops used (ops on explicit zeros not counted as “work”)See p. 375 of Vuduc’s thesis for matrices
Accuracy of the Tuning Heuristics (2/4)
Accuracy of the Tuning Heuristics (2/4)DGEMV
Upper Bounds on Performance for blocked SpMV
• P = (flops) / (time)– Flops = 2 * nnz(A)
• Upper bound on speed: Two main assumptions– 1. Count memory ops only (streaming)– 2. Count only compulsory, capacity misses: ignore conflicts
• Account for line sizes• Account for matrix size and nnz
• Charge minimum access “latency” i at Li cache & mem
– e.g., Saavedra-Barrera and PMaC MAPS benchmarks
1mem11
1memmem
Misses)(Misses)(Loads
HitsHitsTime
iiii
iii
Example: Bounds on Itanium 2
Example: Bounds on Itanium 2
Example: Bounds on Itanium 2
Summary of Other Performance Optimizations
• Optimizations for SpMV– Register blocking (RB): up to 4x over CSR– Variable block splitting: 2.1x over CSR, 1.8x over RB– Diagonals: 2x over CSR– Reordering to create dense structure + splitting: 2x over
CSR– Symmetry: 2.8x over CSR, 2.6x over RB– Cache blocking: 2.8x over CSR– Multiple vectors (SpMM): 7x over CSR– And combinations…
• Sparse triangular solve– Hybrid sparse/dense data structure: 1.8x over CSR
• Higher-level kernels– A·AT·x, AT·A·x: 4x over CSR, 1.8x over RB– A·x: 2x over CSR, 1.5x over RB– [A·x, A·x, A·x, .. , Ak·x] …. more to say later
Potential Impact on Applications: Omega3P• Application: accelerator cavity design [Ko]• Relevant optimization techniques
– Symmetric storage– Register blocking– Reordering, to create more dense blocks
• Reverse Cuthill-McKee ordering to reduce bandwidth– Do Breadth-First-Search, number nodes in reverse order visited
• Traveling Salesman Problem-based ordering to create blocks
– Nodes = columns of A– Weights(u, v) = no. of nz u, v have in common– Tour = ordering of columns– Choose maximum weight tour– See [Pinar & Heath ’97]
• 2.1x speedup on IBM Power 4
Source: Accelerator Cavity Design Problem (Ko via Husbands)
Post-RCM Reordering
100x100 Submatrix Along Diagonal
Before: Green + RedAfter: Green + Blue
“Microscopic” Effect of RCM Reordering
“Microscopic” Effect of Combined RCM+TSP Reordering
Before: Green + RedAfter: Green + Blue
(Omega3P)
Optimized Sparse Kernel Interface - OSKI
• Provides sparse kernels automatically tuned for user’s matrix & machine– BLAS-style functionality: SpMV, Ax & ATy, TrSV– Hides complexity of run-time tuning– Includes new, faster locality-aware kernels: ATAx, Akx
• Faster than standard implementations– Up to 4x faster matvec, 1.8x trisolve, 4x ATA*x
• For “advanced” users & solver library writers– Available as stand-alone library (OSKI 1.0.1h, 6/07)– Available as PETSc extension (OSKI-PETSc .1d, 3/06)– Bebop.cs.berkeley.edu/oski
How the OSKI Tunes (Overview)
Benchmarkdata
1. Build forTargetArch.
2. Benchmark
Heuristicmodels
1. EvaluateModels
Generatedcode
variants
2. SelectData Struct.
& Code
Library Install-Time (offline) Application Run-Time
To user:Matrix handlefor kernelcalls
Workloadfrom program
monitoring
Extensibility: Advanced users may write & dynamically add “Code variants” and “Heuristic models” to system.
HistoryMatrix
How to Call OSKI: Basic Usage
• May gradually migrate existing apps– Step 1: “Wrap” existing data structures– Step 2: Make BLAS-like kernel calls
int* ptr = …, *ind = …; double* val = …; /* Matrix, in CSR format */
double* x = …, *y = …; /* Let x and y be two dense vectors */
/* Compute y = ·y + ·A·x, 500 times */for( i = 0; i < 500; i++ )
my_matmult( ptr, ind, val, , x, , y );
How to Call OSKI: Basic Usage
• May gradually migrate existing apps– Step 1: “Wrap” existing data structures– Step 2: Make BLAS-like kernel calls
int* ptr = …, *ind = …; double* val = …; /* Matrix, in CSR format */
double* x = …, *y = …; /* Let x and y be two dense vectors *//* Step 1: Create OSKI wrappers around this data */oski_matrix_t A_tunable = oski_CreateMatCSR(ptr, ind, val, num_rows,
num_cols, SHARE_INPUTMAT, …);
oski_vecview_t x_view = oski_CreateVecView(x, num_cols, UNIT_STRIDE);
oski_vecview_t y_view = oski_CreateVecView(y, num_rows, UNIT_STRIDE);
/* Compute y = ·y + ·A·x, 500 times */for( i = 0; i < 500; i++ )
my_matmult( ptr, ind, val, , x, , y );
How to Call OSKI: Basic Usage
• May gradually migrate existing apps– Step 1: “Wrap” existing data structures– Step 2: Make BLAS-like kernel calls
int* ptr = …, *ind = …; double* val = …; /* Matrix, in CSR format */
double* x = …, *y = …; /* Let x and y be two dense vectors *//* Step 1: Create OSKI wrappers around this data */oski_matrix_t A_tunable = oski_CreateMatCSR(ptr, ind, val, num_rows,
num_cols, SHARE_INPUTMAT, …);
oski_vecview_t x_view = oski_CreateVecView(x, num_cols, UNIT_STRIDE);
oski_vecview_t y_view = oski_CreateVecView(y, num_rows, UNIT_STRIDE);
/* Compute y = ·y + ·A·x, 500 times */for( i = 0; i < 500; i++ )
oski_MatMult(A_tunable, OP_NORMAL, , x_view, , y_view);/* Step 2 */
How to Call OSKI: Tune with Explicit Hints
• User calls “tune” routine– May provide explicit tuning hints (OPTIONAL)
oski_matrix_t A_tunable = oski_CreateMatCSR( … );
/* … */
/* Tell OSKI we will call SpMV 500 times (workload hint) */oski_SetHintMatMult(A_tunable, OP_NORMAL, , x_view, , y_view, 500);/* Tell OSKI we think the matrix has 8x8 blocks (structural hint) */oski_SetHint(A_tunable, HINT_SINGLE_BLOCKSIZE, 8, 8);
oski_TuneMat(A_tunable); /* Ask OSKI to tune */
for( i = 0; i < 500; i++ )
oski_MatMult(A_tunable, OP_NORMAL, , x_view, , y_view);
How the User Calls OSKI: Implicit Tuning
• Ask library to infer workload– Library profiles all kernel calls– May periodically re-tune
oski_matrix_t A_tunable = oski_CreateMatCSR( … );
/* … */
for( i = 0; i < 500; i++ ) {
oski_MatMult(A_tunable, OP_NORMAL, , x_view, , y_view);oski_TuneMat(A_tunable); /* Ask OSKI to tune */
}
43
Multicore SMPs Used for Tuning SpMVAMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell BladeSun T2+ T5140 (Victoria Falls)
44
Multicore SMPs with Conventional cache-based memory hierarchy
AMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell BladeSun T2+ T5140 (Victoria Falls)
45
Multicore SMPs with local store-based memory hierarchy
AMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell Blade
Sun T2+ T5140 (Victoria Falls)
46
Multicore SMPs with CMT = Chip-MultiThreading
AMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell BladeSun T2+ T5140 (Victoria Falls)
Multicore SMPs: Number of threadsAMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell BladeSun T2+ T5140 (Victoria Falls)
8 threads 8 threads
16* threads128 threads
*SPEs only
Multicore SMPs: peak double precision flops
AMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell BladeSun T2+ T5140 (Victoria Falls)
75 GFlop/s 74 Gflop/s
29* GFlop/s19 GFlop/s
*SPEs only
Multicore SMPs: total DRAM bandwidth
AMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell BladeSun T2+ T5140 (Victoria Falls)
21 GB/s (read)
10 GB/s (write)21 GB/s
51 GB/s42 GB/s (read)
21 GB/s (write)
*SPEs only
Multicore SMPs with Non-Uniform Memory Access - NUMA
AMD Opteron 2356 (Barcelona)Intel Xeon E5345 (Clovertown)
IBM QS20 Cell BladeSun T2+ T5140 (Victoria Falls)
*SPEs only
51
Set of 14 test matrices
• All bigger than the caches of our SMPs
Dense
ProteinFEM /
SpheresFEM /
CantileverWind
TunnelFEM /Harbor
QCDFEM /Ship
Economics Epidemiology
FEM /Accelerator
Circuit webbase
LP
2K x 2K Dense matrixstored in sparse format
Well Structured(sorted by nonzeros/row)
Poorly Structuredhodgepodge
Extreme Aspect Ratio(linear programming)
52
SpMV Performance: Naive parallelization
• Out-of-the box SpMV performance on a suite of 14 matrices
• Scalability isn’t great:
Compare to # threads
8 8 128 16
Naïve Pthreads
Naïve
SpMV Performance: NUMA and Software Prefetching
53
NUMA-aware allocation is essential on NUMA SMPs.
Explicit software prefetching can boost bandwidth and change cache replacement policies
used exhaustive search
SpMV Performance: “Matrix Compression”
54
Compression includes register blocking other formats smaller indices
Use heuristic rather than search
55
SpMV Performance: cache and TLB blocking
+Cache/LS/TLB Blocking
+Matrix Compression
+SW Prefetching
+NUMA/Affinity
Naïve Pthreads
Naïve
56
SpMV Performance: Architecture specific optimizations
+Cache/LS/TLB Blocking
+Matrix Compression
+SW Prefetching
+NUMA/Affinity
Naïve Pthreads
Naïve
57
SpMV Performance: max speedup
• Fully auto-tuned SpMV performance across the suite of matrices
• Included SPE/local store optimized version
• Why do some optimizations work better on some architectures?
+Cache/LS/TLB Blocking
+Matrix Compression
+SW Prefetching
+NUMA/Affinity
Naïve Pthreads
Naïve
2.7x2.7x 4.0x4.0x
2.9x2.9x 35x35x
Avoiding Communication in Sparse Linear Algebra
• k-steps of typical iterative solver for Ax=b or Ax=λx– Does k SpMVs with starting vector (eg with b, if solving Ax=b)– Finds “best” solution among all linear combinations of these k+1
vectors– Many such “Krylov Subspace Methods”
• Conjugate Gradients, GMRES, Lanczos, Arnoldi, …
• Goal: minimize communication in Krylov Subspace Methods– Assume matrix “well-partitioned,” with modest surface-to-volume
ratio– Parallel implementation
• Conventional: O(k log p) messages, because k calls to SpMV• New: O(log p) messages - optimal
– Serial implementation• Conventional: O(k) moves of data from slow to fast memory• New: O(1) moves of data – optimal
• Lots of speed up possible (modeled and measured)– Price: some redundant computation
x
AxA2x
A3x
A4x
A5x
A6x
A7xA8x
Locally Dependent Entries for [x,Ax], A tridiagonal, 2 processors
Can be computed without communication
Proc 1 Proc 2
x
AxA2x
A3x
A4x
A5x
A6x
A7xA8x
Can be computed without communication
Proc 1 Proc 2
Locally Dependent Entries for [x,Ax,A2x], A tridiagonal, 2 processors
x
AxA2x
A3x
A4x
A5x
A6x
A7xA8x
Can be computed without communication
Proc 1 Proc 2
Locally Dependent Entries for [x,Ax,…,A3x], A tridiagonal, 2 processors
x
AxA2x
A3x
A4x
A5x
A6x
A7xA8x
Can be computed without communication
Proc 1 Proc 2
Locally Dependent Entries for [x,Ax,…,A4x], A tridiagonal, 2 processors
x
AxA2x
A3x
A4x
A5x
A6x
A7xA8x
Locally Dependent Entries for [x,Ax,…,A8x], A tridiagonal, 2 processors
Can be computed without communicationk=8 fold reuse of A
Proc 1 Proc 2
Remotely Dependent Entries for [x,Ax,…,A8x], A tridiagonal, 2 processors
x
AxA2x
A3x
A4x
A5x
A6x
A7xA8x
One message to get data needed to compute remotely dependent entries, not k=8Minimizes number of messages = latency cost
Price: redundant work “surface/volume ratio”
Proc 1 Proc 2
Remotely Dependent Entries for [x,Ax,A2x,A3x], A irregular, multiple processors
Sequential [x,Ax,…,A4x], with memory hierarchy
v
One read of matrix from slow memory, not k=4Minimizes words moved = bandwidth cost
No redundant work
Performance results on 8-Core Clovertown
Optimizing Communication Complexity of Sparse Solvers
• Example: GMRES for Ax=b on “2D Mesh”– x lives on n-by-n mesh– Partitioned on p½ -by- p½ grid of p processors– A has “5 point stencil” (Laplacian)
• (Ax)(i,j) = linear_combination(x(i,j), x(i,j±1), x(i±1,j))
– Ex: 18-by-18 mesh on 3-by-3 grid of 9 processors
Minimizing Communication of GMRES• What is the cost = (#flops, #words, #mess)
of k steps of standard GMRES?
GMRES, ver.1: for i=1 to k w = A * v(i-1) MGS(w, v(0),…,v(i-1)) update v(i), H endfor solve LSQ problem with H
n/p½
n/p½
• Cost(A * v) = k * (9n2 /p, 4n / p½ , 4 )• Cost(MGS) = k2/2 * ( 4n2 /p , log p , log p )• Total cost ~ Cost( A * v ) + Cost (MGS)• Can we reduce the latency?
Minimizing Communication of GMRES• Cost(GMRES, ver.1) = Cost(A*v) + Cost(MGS)
• Cost(W) = ( ~ same, ~ same , 8 )• Latency cost independent of k – optimal
• Cost (MGS) unchanged• Can we reduce the latency more?
= ( 9kn2 /p, 4kn / p½ , 4k ) + ( 2k2n2 /p , k2 log p / 2 , k2 log p / 2 )• How much latency cost from A*v can you avoid? Almost all
GMRES, ver. 2: W = [ v, Av, A2v, … , Akv ] [Q,R] = MGS(W) Build H from R, solve LSQ problem
k = 3
Minimizing Communication of GMRES• Cost(GMRES, ver. 2) = Cost(W) + Cost(MGS)
= ( 9kn2 /p, 4kn / p½ , 8 ) + ( 2k2n2 /p , k2 log p / 2 , k2 log p / 2 )• How much latency cost from MGS can you avoid? Almost all
• Cost(TSQR) = ( ~ same, ~ same , log p )• Latency cost independent of s - optimal
GMRES, ver. 3: W = [ v, Av, A2v, … , Akv ] [Q,R] = TSQR(W) … “Tall Skinny QR” Build H from R, solve LSQ problem
W = W1
W2
W3
W4
R1
R2
R3
R4
R12
R34
R1234
Minimizing Communication of GMRES• Cost(GMRES, ver. 2) = Cost(W) + Cost(MGS)
= ( 9kn2 /p, 4kn / p½ , 8 ) + ( 2k2n2 /p , k2 log p / 2 , k2 log p / 2 )• How much latency cost from MGS can you avoid? Almost all
GMRES, ver. 3: W = [ v, Av, A2v, … , Akv ] [Q,R] = TSQR(W) … “Tall Skinny QR” Build H from R, solve LSQ problem
W = W1
W2
W3
W4
R1
R2
R3
R4
R12
R34
R1234
• Cost(TSQR) = ( ~ same, ~ same , log p )• Oops
Minimizing Communication of GMRES• Cost(GMRES, ver. 2) = Cost(W) + Cost(MGS)
= ( 9kn2 /p, 4kn / p½ , 8 ) + ( 2k2n2 /p , k2 log p / 2 , k2 log p / 2 )• How much latency cost from MGS can you avoid? Almost all
GMRES, ver. 3: W = [ v, Av, A2v, … , Akv ] [Q,R] = TSQR(W) … “Tall Skinny QR” Build H from R, solve LSQ problem
W = W1
W2
W3
W4
R1
R2
R3
R4
R12
R34
R1234
• Cost(TSQR) = ( ~ same, ~ same , log p )• Oops – W from power method, precision lost!
Minimizing Communication of GMRES
• Cost(GMRES, ver. 3) = Cost(W) + Cost(TSQR)
= ( 9kn2 /p, 4kn / p½ , 8 ) + ( 2k2n2 /p , k2 log p / 2 , log p )• Latency cost independent of k, just log p – optimal• Oops – W from power method, so precision lost – What to do?
• Use a different polynomial basis• Not Monomial basis W = [v, Av, A2v, …], instead …• Newton Basis WN = [v, (A – θ1 I)v , (A – θ2 I)(A – θ1 I)v, …] or• Chebyshev Basis WC = [v, T1(v), T2(v), …]
Speed ups on 8-core Clovertown
Conclusions• Fast code must minimize communication
– Especially for sparse matrix computations because communication dominates
• Generating fast code for a single SpMV– Design space of possible algorithms must be
searched at run-time, when sparse matrix available– Design space should be searched automatically
• Biggest speedups from minimizing communication in an entire sparse solver– Many more opportunities to minimize communication
in multiple SpMVs than in one– Requires transforming entire algorithm– Lots of open problems
EXTRA SLIDES
Quick-and-dirty Parallelism: OSKI-PETSc
• Extend PETSc’s distributed memory SpMV (MATMPIAIJ)
p0
p1
p2
p3
• PETSc– Each process stores
diag (all-local) and off-diag submatrices
• OSKI-PETSc:– Add OSKI wrappers– Each submatrix
tuned independently
OSKI-PETSc Proof-of-Concept Results
• Matrix 1: Accelerator cavity design (R. Lee @ SLAC)– N ~ 1 M, ~40 M non-zeros– 2x2 dense block substructure– Symmetric
• Matrix 2: Linear programming (Italian Railways)– Short-and-fat: 4k x 1M, ~11M non-zeros– Highly unstructured– Big speedup from cache-blocking: no native PETSc
format
• Evaluation machine: Xeon cluster– Peak: 4.8 Gflop/s per node
Accelerator Cavity Matrix
OSKI-PETSc Performance: Accel. Cavity
Linear Programming Matrix
…
OSKI-PETSc Performance: LP Matrix
Performance Results• Measured Multicore (Clovertown) speedups up to 6.4x• Measured/Modeled sequential OOC speedup up to 3x• Modeled parallel Petascale speedup up to 6.9x• Modeled parallel Grid speedup up to 22x
• Sequential speedup due to bandwidth, works for many problem sizes
• Parallel speedup due to latency, works for smaller problems on many processors
Speedups on Intel Clovertown (8 core)
Extensions
• Other Krylov methods– Arnoldi, CG, Lanczos, …
• Preconditioning– Solve MAx=Mb where preconditioning matrix M chosen to
make system “easier” • M approximates A-1 somehow, but cheaply, to accelerate
convergence
– Cheap as long as contributions from “distant” parts of the system can be compressed
• Sparsity• Low rank
Design Space for [x,Ax,…,Akx] (1/3)
• Mathematical Operation– Keep all vectors
• Krylov Subspace Methods
– Improving conditioning of basis• W = [x, p1(A)x, p2(A)x,…,pk(A)x]
• pi(A) = degree i polynomial chosen to reduce cond(W)
– Preconditioning (Ay=b MAy=Mb)• [x,Ax,MAx,AMAx,MAMAx,…,(MA)kx]
– Keep last vector Akx only• Jacobi, Gauss Seidel
Design Space for [x,Ax,…,Akx] (2/3)
• Representation of sparse A– Zero pattern may be explicit or implicit– Nonzero entries may be explicit or implicit
• Implicit save memory, communication
Explicit pattern Implicit pattern
Explicit nonzeros General sparse matrix
Image segmentation
Implicit nonzeros Laplacian(graph), for graph partitioning
“Stencil matrix”Ex: tridiag(-1,2,-1)
• Representation of dense preconditioners M
– Low rank off-diagonal blocks (semiseparable)
Design Space for [x,Ax,…,Akx] (3/3)
• Parallel implementation– From simple indexing, with redundant flops
surface/volume ratio– To complicated indexing, with fewer redundant flops
• Sequential implementation– Depends on whether vectors fit in fast memory
• Reordering rows, columns of A– Important in parallel and sequential cases– Can be reduced to pair of Traveling Salesmen Problems
• Plus all the optimizations for one SpMV!
Summary • Communication-Avoiding Linear Algebra (CALA)• Lots of related work
– Some going back to 1960’s– Reports discuss this comprehensively, not here
• Our contributions– Several new algorithms, improvements on old ones– Unifying parallel and sequential approaches to avoiding
communication– Time for these algorithms has come, because of growing
communication costs– Why avoid communication just for linear algebra motifs?
Possible Class Projects
• Come to BEBOP meetings (T 9 – 10:30, 606 Soda)
• Incorporate multicore optimizations into OSKI• Experiment with SpMV on GPU
– Which optimizations are most effective?
• Try to speed up particular matrices of interest– Data mining
• Experiment with new [x,Ax,…,Akx] kernel– GPU, multicore, distributed memory– On matrices of interest– Experiment with new solvers using this kernel
Extra Slides
Tuning Higher Level Algorithms
• So far we have tuned a single sparse matrix kernel• y = AT*A*x motivated by higher level algorithm (SVD)
• What can we do by extending tuning to a higher level?• Consider Krylov subspace methods for Ax=b, Ax = x
• Conjugate Gradients (CG), GMRES, Lanczos, …• Inner loop does y=A*x, dot products, saxpys, scalar ops• Inner loop costs at least O(1) messages• k iterations cost at least O(k) messages
• Our goal: show how to do k iterations with O(1) messages• Possible payoff – make Krylov subspace methods much faster on machines with slow networks• Memory bandwidth improvements too (not discussed)• Obstacles: numerical stability, preconditioning, …
Krylov Subspace Methods for Solving Ax=b
• Compute a basis for a subspace V by doing y = A*x k times
• Find “best” solution in that Krylov subspace V
• Given starting vector x1, V spanned by x2 = A*x1, x3 = A*x2 , … , xk = A*xk-1
• GMRES finds an orthogonal basis of V by Gram-Schmidt, so it actually does a different set of SpMVs than in last bullet
Example: Standard GMRES
r = b - A*x1, = length(r), v1 = r / … length(r) = sqrt( ri2 )
for m= 1 to k do w = A*vm … at least O(1) messages
for i = 1 to m do … Gram-Schmidt him = dotproduct(vi , w ) … at least O(1) messages, or log(p)
w = w – h im * vi
end for hm+1,m = length(w) … at least O(1) messages, or log(p)
vm+1 = w / hm+1,m
end forfind y minimizing length( Hk * y – e1 ) … small, local problem
new x = x1 + Vk * y … Vk = [v1 , v2 , … , vk ]
O(k2), or O(k2 log p), messages altogether
Latency-Avoiding GMRES (1)
r = b - A*x1, = length(r), v1 = r / …O(log p) messages
Wk+1 = [v1 , A * v1 , A2 * v1 , … , Ak * v1 ] … O(1) messages
[Q, R] = qr(Wk+1) … QR decomposition, O(log p) messages
Hk = R(:, 2:k+1) * (R(1:k,1:k))-1 … small, local problem
find y minimizing length( Hk * y – e1 ) … small, local problem
new x = x1 + Qk * y … local problemO(log p) messages altogether
Independent of k
Latency-Avoiding GMRES (2)
[Q, R] = qr(Wk+1) … QR decomposition, O(log p) messages
Easy, but not so stable way to do it:
X(myproc) = Wk+1T(myproc) * Wk+1 (myproc)
… local computation Y = sum_reduction(X(myproc)) … O(log p) messages
… Y = Wk+1T* Wk+1
R = (cholesky(Y))T … small, local computation
Q(myproc) = Wk+1 (myproc) * R-1 … local computation
Numerical example (1)
Diagonal matrix with n=1000, Aii from 1 down to 10-5
Instability as k grows, after many iterations
Numerical Example (2)Partial remedy: restarting periodically (every 120 iterations)
Other remedies: high precision, different basis than [v , A * v , … , Ak * v ]
Operation Counts for [Ax,A2x,A3x,…,Akx] on p procs
Problem Per-proc cost
Standard Optimized
1D mesh #messages 2k 2
(tridiagonal)
#words sent
2k 2k
#flops 5kn/p 5kn/p + 5k2
memory (k+4)n/p (k+4)n/p + 8k3D mesh #messages 26k 26
27 pt stencil
#words sent
6kn2p-2/3 + 12knp-1/3
+ O(k)6kn2p-2/3 + 12k2np-1/3
+ O(k3)
#flops 53kn3/p 53kn3/p + O(k2n2p-2/3)
memory (k+28)n3/p+ 6n2p-2/3 + O(np-1/3)
(k+28)n3/p + 168kn2p-2/3 + O(k2np-1/3)
Summary and Future Work
• Dense– LAPACK– ScaLAPACK
• Communication primitives• Sparse
– Kernels, Stencils– Higher level algorithms
• All of the above on new architectures– Vector, SMPs, multicore, Cell, …
• High level support for tuning– Specification language– Integration into compilers
Extra Slides
A Sparse Matrix You Encounter Every Day
Who am I?
I am aBig Repository
Of usefulAnd uselessFacts alike.
Who am I?
(Hint: Not your e-mail inbox.)
What about the Google Matrix?
• Google approach– Approx. once a month: rank all pages using connectivity
structure• Find dominant eigenvector of a matrix
– At query-time: return list of pages ordered by rank• Matrix: A = G + (1-)(1/n)uuT
– Markov model: Surfer follows link with probability , jumps to a random page with probability 1-
– G is n x n connectivity matrix [n billions]• gij is non-zero if page i links to page j• Normalized so each column sums to 1• Very sparse: about 7—8 non-zeros per row (power law dist.)
– u is a vector of all 1 values– Steady-state probability xi of landing on page i is solution to x
= Ax
• Approximate x by power method: x = Akx0
– In practice, k 25
Current Work
• Public software release• Impact on library designs: Sparse BLAS, Trilinos, PETSc,
…• Integration in large-scale applications
– DOE: Accelerator design; plasma physics– Geophysical simulation based on Block Lanczos (ATA*X; LBL)
• Systematic heuristics for data structure selection?• Evaluation of emerging architectures
– Revisiting vector micros
• Other sparse kernels– Matrix triple products, Ak*x
• Parallelism• Sparse benchmarks (with UTK) [Gahvari & Hoemmen]• Automatic tuning of MPI collective ops [Nishtala, et al.]
Summary of High-Level Themes
• “Kernel-centric” optimization– Vs. basic block, trace, path optimization, for instance– Aggressive use of domain-specific knowledge
• Performance bounds modeling– Evaluating software quality– Architectural characterizations and consequences
• Empirical search– Hybrid on-line/run-time models
• Statistical performance models– Exploit information from sampling, measuring
Related Work
• My bibliography: 337 entries so far• Sample area 1: Code generation
– Generative & generic programming– Sparse compilers– Domain-specific generators
• Sample area 2: Empirical search-based tuning– Kernel-centric
• linear algebra, signal processing, sorting, MPI, …
– Compiler-centric• profiling + FDO, iterative compilation, superoptimizers,
self-tuning compilers, continuous program optimization
Future Directions (A Bag of Flaky Ideas)
• Composable code generators and search spaces• New application domains
– PageRank: multilevel block algorithms for topic-sensitive search?
• New kernels: cryptokernels– rich mathematical structure germane to performance; lots
of hardware
• New tuning environments– Parallel, Grid, “whole systems”
• Statistical models of application performance– Statistical learning of concise parametric models from
traces for architectural evaluation– Compiler/automatic derivation of parametric models
Possible Future Work
• Different Architectures– New FP instruction sets (SSE2)– SMP / multicore platforms– Vector architectures
• Different Kernels• Higher Level Algorithms
– Parallel versions of kenels, with optimized communication– Block algorithms (eg Lanczos)– XBLAS (extra precision)
• Produce Benchmarks– Augment HPCC Benchmark
• Make it possible to combine optimizations easily for any kernel
• Related tuning activities (LAPACK & ScaLAPACK)
Review of Tuning by Illustration
(Extra Slides)
Splitting for Variable Blocks and Diagonals
• Decompose A = A1 + A2 + … At
– Detect “canonical” structures (sampling)– Split
– Tune each Ai
– Improve performance and save storage
• New data structures– Unaligned block CSR
• Relax alignment in rows & columns
– Row-segmented diagonals
Example: Variable Block Row (Matrix #12)
2.1x over CSR1.8x over RB
Example: Row-Segmented Diagonals
2x over CSR
Mixed Diagonal and Block Structure
Summary
• Automated block size selection– Empirical modeling and search– Register blocking for SpMV, triangular solve, ATA*x
• Not fully automated– Given a matrix, select splittings and transformations
• Lots of combinatorial problems– TSP reordering to create dense blocks (Pinar ’97;
Moon, et al. ’04)
Extra Slides
A Sparse Matrix You Encounter Every Day
Who am I?
I am aBig Repository
Of usefulAnd uselessFacts alike.
Who am I?
(Hint: Not your e-mail inbox.)
Problem Context
• Sparse kernels abound– Models of buildings, cars, bridges, economies, …– Google PageRank algorithm
• Historical trends– Sparse matrix-vector multiply (SpMV): 10% of peak– 2x faster with “hand-tuning”– Tuning becoming more difficult over time– Promise of automatic tuning: PHiPAC/ATLAS, FFTW, …
• Challenges to high-performance– Not dense linear algebra!
• Complex data structures: indirect, irregular memory access
• Performance depends strongly on run-time inputs
Key Questions, Ideas, Conclusions
• How to tune basic sparse kernels automatically?– Empirical modeling and search
• Up to 4x speedups for SpMV• 1.8x for triangular solve• 4x for ATA*x; 2x for A2*x• 7x for multiple vectors
• What are the fundamental limits on performance?– Kernel-, machine-, and matrix-specific upper bounds
• Achieve 75% or more for SpMV, limiting low-level tuning• Consequences for architecture?
• General techniques for empirical search-based tuning?– Statistical models of performance
Road Map
• Sparse matrix-vector multiply (SpMV) in a nutshell• Historical trends and the need for search• Automatic tuning techniques• Upper bounds on performance• Statistical models of performance
Matrix-vector multiply kernel: y(i) y(i) + A(i,j)*x(j)Matrix-vector multiply kernel: y(i) y(i) + A(i,j)*x(j)
for each row i
for k=ptr[i] to ptr[i+1] do
y[i] = y[i] + val[k]*x[ind[k]]
Compressed Sparse Row (CSR) Storage
Matrix-vector multiply kernel: y(i) y(i) + A(i,j)*x(j)
for each row i
for k=ptr[i] to ptr[i+1] do
y[i] = y[i] + val[k]*x[ind[k]]
Road Map
• Sparse matrix-vector multiply (SpMV) in a nutshell• Historical trends and the need for search• Automatic tuning techniques• Upper bounds on performance• Statistical models of performance
Historical Trends in SpMV Performance
• The Data– Uniprocessor SpMV performance since 1987– “Untuned” and “Tuned” implementations– Cache-based superscalar micros; some vectors– LINPACK
SpMV Historical Trends: Mflop/s
Example: The Difficulty of Tuning
• n = 21216• nnz = 1.5 M• kernel: SpMV
• Source: NASA structural analysis problem
Still More Surprises
• More complicated non-zero structure in general
Still More Surprises
• More complicated non-zero structure in general
• Example: 3x3 blocking– Logical grid of 3x3 cells
Historical Trends: Mixed News
• Observations– Good news: Moore’s law like behavior– Bad news: “Untuned” is 10% peak or less,
worsening– Good news: “Tuned” roughly 2x better today, and
improving– Bad news: Tuning is complex
– (Not really news: SpMV is not LINPACK)
• Questions– Application: Automatic tuning?– Architect: What machines are good for SpMV?
Road Map
• Sparse matrix-vector multiply (SpMV) in a nutshell• Historical trends and the need for search• Automatic tuning techniques
– SpMV [SC’02; IJHPCA ’04b]– Sparse triangular solve (SpTS) [ICS/POHLL ’02]– ATA*x [ICCS/WoPLA ’03]
• Upper bounds on performance• Statistical models of performance
SPARSITY: Framework for Tuning SpMV
• SPARSITY: Automatic tuning for SpMV [Im & Yelick ’99]– General approach
• Identify and generate implementation space• Search space using empirical models & experiments
– Prototype library and heuristic for choosing register block size
• Also: cache-level blocking, multiple vectors
• What’s new?– New block size selection heuristic
• Within 10% of optimal — replaces previous version
– Expanded implementation space• Variable block splitting, diagonals, combinations
– New kernels: sparse triangular solve, ATA*x, A*x
Automatic Register Block Size Selection
• Selecting the r x c block size– Off-line benchmark: characterize the machine
• Precompute Mflops(r,c) using dense matrix for each r x c• Once per machine/architecture
– Run-time “search”: characterize the matrix• Sample A to estimate Fill(r,c) for each r x c
– Run-time heuristic model• Choose r, c to maximize Mflops(r,c) / Fill(r,c)
• Run-time costs– Up to ~40 SpMVs (empirical worst case)
Accuracy of the Tuning Heuristics (1/4)
NOTE: “Fair” flops used (ops on explicit zeros not counted as “work”)
DGEMV
Accuracy of the Tuning Heuristics (2/4)DGEMV
Accuracy of the Tuning Heuristics (3/4)DGEMV
Accuracy of the Tuning Heuristics (4/4)DGEMV
Road Map
• Sparse matrix-vector multiply (SpMV) in a nutshell• Historical trends and the need for search• Automatic tuning techniques• Upper bounds on performance
– SC’02
• Statistical models of performance
Motivation for Upper Bounds Model
• Questions– Speedups are good, but what is the speed limit?
• Independent of instruction scheduling, selection
– What machines are “good” for SpMV?
Upper Bounds on Performance: Blocked SpMV
• P = (flops) / (time)– Flops = 2 * nnz(A)
• Lower bound on time: Two main assumptions– 1. Count memory ops only (streaming)– 2. Count only compulsory, capacity misses: ignore conflicts
• Account for line sizes• Account for matrix size and nnz
• Charge min access “latency” i at Li cache & mem
– e.g., Saavedra-Barrera and PMaC MAPS benchmarks
1mem11
1memmem
Misses)(Misses)(Loads
HitsHitsTime
iiii
iii
Example: Bounds on Itanium 2
Example: Bounds on Itanium 2
Example: Bounds on Itanium 2
Fraction of Upper Bound Across Platforms
Achieved Performance and Machine Balance
• Machine balance [Callahan ’88; McCalpin ’95]– Balance = Peak Flop Rate / Bandwidth (flops /
double)
• Ideal balance for mat-vec: 2 flops / double– For SpMV, even less
• SpMV ~ streaming– 1 / (avg load time to stream 1 array) ~ (bandwidth)– “Sustained” balance = peak flops / model bandwidth
i
iii Misses)(Misses)(LoadsTime mem11
Where Does the Time Go?
• Most time assigned to memory• Caches “disappear” when line sizes are equal
– Strictly increasing line sizes
1
memmem HitsHitsTimei
ii
Execution Time Breakdown: Matrix 40
Speedups with Increasing Line Size
Summary: Performance Upper Bounds
• What is the best we can do for SpMV?– Limits to low-level tuning of blocked implementations– Refinements?
• What machines are good for SpMV?– Partial answer: balance characterization
• Architectural consequences?– Example: Strictly increasing line sizes
Road Map
• Sparse matrix-vector multiply (SpMV) in a nutshell• Historical trends and the need for search• Automatic tuning techniques• Upper bounds on performance• Tuning other sparse kernels• Statistical models of performance
– FDO ’00; IJHPCA ’04a
Statistical Models for Automatic Tuning
• Idea 1: Statistical criterion for stopping a search– A general search model
• Generate implementation• Measure performance• Repeat
– Stop when probability of being within of optimal falls below threshold
• Can estimate distribution on-line
• Idea 2: Statistical performance models– Problem: Choose 1 among m implementations at run-
time– Sample performance off-line, build statistical model
Example: Select a Matmul Implementation
Example: Support Vector Classification
Road Map
• Sparse matrix-vector multiply (SpMV) in a nutshell• Historical trends and the need for search• Automatic tuning techniques• Upper bounds on performance• Tuning other sparse kernels• Statistical models of performance• Summary and Future Work
Summary of High-Level Themes
• “Kernel-centric” optimization– Vs. basic block, trace, path optimization, for instance– Aggressive use of domain-specific knowledge
• Performance bounds modeling– Evaluating software quality– Architectural characterizations and consequences
• Empirical search– Hybrid on-line/run-time models
• Statistical performance models– Exploit information from sampling, measuring
Related Work
• My bibliography: 337 entries so far• Sample area 1: Code generation
– Generative & generic programming– Sparse compilers– Domain-specific generators
• Sample area 2: Empirical search-based tuning– Kernel-centric
• linear algebra, signal processing, sorting, MPI, …
– Compiler-centric• profiling + FDO, iterative compilation, superoptimizers,
self-tuning compilers, continuous program optimization
Future Directions (A Bag of Flaky Ideas)
• Composable code generators and search spaces• New application domains
– PageRank: multilevel block algorithms for topic-sensitive search?
• New kernels: cryptokernels– rich mathematical structure germane to performance; lots
of hardware
• New tuning environments– Parallel, Grid, “whole systems”
• Statistical models of application performance– Statistical learning of concise parametric models from
traces for architectural evaluation– Compiler/automatic derivation of parametric models
Acknowledgements
• Super-advisors: Jim and Kathy• Undergraduate R.A.s: Attila, Ben, Jen, Jin,
Michael, Rajesh, Shoaib, Sriram, Tuyet-Linh• See pages xvi—xvii of dissertation.
TSP-based Reordering: Before
(Pinar ’97;Moon, et al ‘04)
TSP-based Reordering: After
(Pinar ’97;Moon, et al ‘04)
Up to 2xspeedupsover CSR
Example: L2 Misses on Itanium 2
Misses measured using PAPI [Browne ’00]
Example: Distribution of Blocked Non-Zeros
Sparse/Dense Partitioning for SpTS
• Partition L into sparse (L1,L2) and dense LD:
2
1
2
1
2
1
b
b
x
x
LL
L
D
• Perform SpTS in three steps:
22
1222
111
ˆ)3(
ˆ)2(
)1(
bxL
xLbb
bxL
D
• Sparsity optimizations for (1)—(2); DTRSV for (3)• Tuning parameters: block size, size of dense triangle
SpTS Performance: Power3
Summary of SpTS and AAT*x Results
• SpTS — Similar to SpMV– 1.8x speedups; limited benefit from low-level tuning
• AATx, ATAx– Cache interleaving only: up to 1.6x speedups– Reg + cache: up to 4x speedups
• 1.8x speedup over register only
– Similar heuristic; same accuracy (~ 10% optimal)– Further from upper bounds: 60—80%
• Opportunity for better low-level tuning a la PHiPAC/ATLAS
• Matrix triple products? Ak*x?– Preliminary work
Register Blocking: Speedup
Register Blocking: Performance
Register Blocking: Fraction of Peak
Example: Confidence Interval Estimation
Costs of Tuning
Splitting + UBCSR: Pentium III
Splitting + UBCSR: Power4
Splitting+UBCSR Storage: Power4
Example: Variable Block Row (Matrix #13)
Preliminary Results (Matrix Set 2): Itanium 2
Web/IR
Dense FEM FEM (var) Bio LPEcon Stat
Multiple Vector Performance
What about the Google Matrix?
• Google approach– Approx. once a month: rank all pages using connectivity
structure• Find dominant eigenvector of a matrix
– At query-time: return list of pages ordered by rank• Matrix: A = G + (1-)(1/n)uuT
– Markov model: Surfer follows link with probability , jumps to a random page with probability 1-
– G is n x n connectivity matrix [n 3 billion]• gij is non-zero if page i links to page j• Normalized so each column sums to 1• Very sparse: about 7—8 non-zeros per row (power law dist.)
– u is a vector of all 1 values– Steady-state probability xi of landing on page i is solution to x
= Ax
• Approximate x by power method: x = Akx0
– In practice, k 25
MAPS Benchmark Example: Power4
MAPS Benchmark Example: Itanium 2
Saavedra-Barrera Example: Ultra 2i
Execution Time Breakdown (PAPI): Matrix 40
Preliminary Results (Matrix Set 1): Itanium 2
LPFEM FEM (var) AssortedDense
Tuning Sparse Triangular Solve (SpTS)
• Compute x=L-1*b where L sparse lower triangular, x & b dense
• L from sparse LU has rich dense substructure– Dense trailing triangle can account for 20—90% of
matrix non-zeros
• SpTS optimizations– Split into sparse trapezoid and dense trailing triangle– Use tuned dense BLAS (DTRSV) on dense triangle– Use Sparsity register blocking on sparse part
• Tuning parameters– Size of dense trailing triangle– Register block size
Sparse Kernels and Optimizations
• Kernels– Sparse matrix-vector multiply (SpMV): y=A*x– Sparse triangular solve (SpTS): x=T-1*b– y=AAT*x, y=ATA*x– Powers (y=Ak*x), sparse triple-product (R*A*RT), …
• Optimization techniques (implementation space)– Register blocking– Cache blocking– Multiple dense vectors (x)– A has special structure (e.g., symmetric, banded, …)– Hybrid data structures (e.g., splitting, switch-to-
dense, …)– Matrix reordering
• How and when do we search?– Off-line: Benchmark implementations– Run-time: Estimate matrix properties, evaluate
performance models based on benchmark data
Cache Blocked SpMV on LSI Matrix: Ultra 2i
A10k x 255k3.7M non-zeros
Baseline:16 Mflop/s
Best block size& performance:16k x 64k28 Mflop/s
Cache Blocking on LSI Matrix: Pentium 4
A10k x 255k3.7M non-zeros
Baseline:44 Mflop/s
Best block size& performance:16k x 16k210 Mflop/s
Cache Blocked SpMV on LSI Matrix: Itanium
A10k x 255k3.7M non-zeros
Baseline:25 Mflop/s
Best block size& performance:16k x 32k72 Mflop/s
Cache Blocked SpMV on LSI Matrix: Itanium 2
A10k x 255k3.7M non-zeros
Baseline:170 Mflop/s
Best block size& performance:16k x 65k275 Mflop/s
Inter-Iteration Sparse Tiling (1/3)
• [Strout, et al., ‘01]• Let A be 6x6 tridiagonal• Consider y=A2x
– t=Ax, y=At
• Nodes: vector elements• Edges: matrix elements
aij
y1
y2
y3
y4
y5
t1
t2
t3
t4
t5
x1
x2
x3
x4
x5
Inter-Iteration Sparse Tiling (2/3)
• [Strout, et al., ‘01]• Let A be 6x6 tridiagonal• Consider y=A2x
– t=Ax, y=At
• Nodes: vector elements• Edges: matrix elements
aij
• Orange = everything needed to compute y1
– Reuse a11, a12
y1
y2
y3
y4
y5
t1
t2
t3
t4
t5
x1
x2
x3
x4
x5
Inter-Iteration Sparse Tiling (3/3)
• [Strout, et al., ‘01]• Let A be 6x6 tridiagonal• Consider y=A2x
– t=Ax, y=At
• Nodes: vector elements• Edges: matrix elements
aij
• Orange = everything needed to compute y1
– Reuse a11, a12
• Grey = y2, y3
– Reuse a23, a33, a43
y1
y2
y3
y4
y5
t1
t2
t3
t4
t5
x1
x2
x3
x4
x5
Inter-Iteration Sparse Tiling: Issues
• Tile sizes (colored regions) grow with no. of iterations and increasing out-degree– G likely to have a few
nodes with high out-degree (e.g., Yahoo)
• Mathematical tricks to limit tile size?– Judicious dropping of
edges [Ng’01]
y1
y2
y3
y4
y5
t1
t2
t3
t4
t5
x1
x2
x3
x4
x5
Summary and Questions
• Need to understand matrix structure and machine– BeBOP: suite of techniques to deal with different sparse
structures and architectures• Google matrix problem
– Established techniques within an iteration– Ideas for inter-iteration optimizations– Mathematical structure of problem may help
• Questions– Structure of G?– What are the computational bottlenecks?– Enabling future computations?
• E.g., topic-sensitive PageRank multiple vector version [Haveliwala ’02]
– See www.cs.berkeley.edu/~richie/bebop/intel/google for more info, including more complete Itanium 2 results.
Exploiting Matrix Structure
• Symmetry (numerical or structural)– Reuse matrix entries– Can combine with register blocking, multiple vectors,
…
• Matrix splitting– Split the matrix, e.g., into r x c and 1 x 1– No fill overhead
• Large matrices with random structure– E.g., Latent Semantic Indexing (LSI) matrices– Technique: cache blocking
• Store matrix as 2i x 2j sparse submatrices• Effective when x vector is large• Currently, search to find fastest size
Symmetric SpMV Performance: Pentium 4
SpMV with Split Matrices: Ultra 2i
Cache Blocking on Random Matrices: Itanium
Speedup on four bandedrandom matrices.
Sparse Kernels and Optimizations
• Kernels– Sparse matrix-vector multiply (SpMV): y=A*x– Sparse triangular solve (SpTS): x=T-1*b– y=AAT*x, y=ATA*x– Powers (y=Ak*x), sparse triple-product (R*A*RT), …
• Optimization techniques (implementation space)– Register blocking– Cache blocking– Multiple dense vectors (x)– A has special structure (e.g., symmetric, banded, …)– Hybrid data structures (e.g., splitting, switch-to-dense, …)– Matrix reordering
• How and when do we search?– Off-line: Benchmark implementations– Run-time: Estimate matrix properties, evaluate
performance models based on benchmark data
Register Blocked SpMV: Pentium III
Register Blocked SpMV: Ultra 2i
Register Blocked SpMV: Power3
Register Blocked SpMV: Itanium
Possible Optimization Techniques
• Within an iteration, i.e., computing (G+uuT)*x once– Cache block G*x
• On linear programming matrices and matrices with random structure (e.g., LSI), 1.5—4x speedups
• Best block size is matrix and machine dependent
– Reordering and/or splitting of G to separate dense structure (rows, columns, blocks)
• Between iterations, e.g., (G+uuT)2x– (G+uuT)2x = G2x + (Gu)uTx + u(uTG)x + u(uTu)uTx
• Compute Gu, uTG, uTu once for all iterations• G2x: Inter-iteration tiling to read G only once
Multiple Vector Performance: Itanium
Sparse Kernels and Optimizations
• Kernels– Sparse matrix-vector multiply (SpMV): y=A*x– Sparse triangular solve (SpTS): x=T-1*b– y=AAT*x, y=ATA*x– Powers (y=Ak*x), sparse triple-product (R*A*RT), …
• Optimization techniques (implementation space)– Register blocking– Cache blocking– Multiple dense vectors (x)– A has special structure (e.g., symmetric, banded, …)– Hybrid data structures (e.g., splitting, switch-to-
dense, …)– Matrix reordering
• How and when do we search?– Off-line: Benchmark implementations– Run-time: Estimate matrix properties, evaluate
performance models based on benchmark data
SpTS Performance: Itanium
(See POHLL ’02 workshop paper, at ICS ’02.)
Sparse Kernels and Optimizations
• Kernels– Sparse matrix-vector multiply (SpMV): y=A*x– Sparse triangular solve (SpTS): x=T-1*b– y=AAT*x, y=ATA*x– Powers (y=Ak*x), sparse triple-product (R*A*RT), …
• Optimization techniques (implementation space)– Register blocking– Cache blocking– Multiple dense vectors (x)– A has special structure (e.g., symmetric, banded, …)– Hybrid data structures (e.g., splitting, switch-to-dense, …)– Matrix reordering
• How and when do we search?– Off-line: Benchmark implementations– Run-time: Estimate matrix properties, evaluate
performance models based on benchmark data
Optimizing AAT*x
• Kernel: y=AAT*x, where A is sparse, x & y dense– Arises in linear programming, computation of SVD– Conventional implementation: compute z=AT*x, y=A*z
• Elements of A can be reused:
n
k
Tkk
Tn
T
n xaax
a
a
aay1
1
1 )(
• When ak represent blocks of columns, can apply register blocking.
Optimized AAT*x Performance: Pentium III
Current Directions
• Applying new optimizations– Other split data structures (variable block, diagonal,
…)– Matrix reordering to create block structure– Structural symmetry
• New kernels (triple product RART, powers Ak, …)• Tuning parameter selection• Building an automatically tuned sparse matrix
library– Extending the Sparse BLAS– Leverage existing sparse compilers as code
generation infrastructure– More thoughts on this topic tomorrow
Related Work
• Automatic performance tuning systems– PHiPAC [Bilmes, et al., ’97], ATLAS [Whaley & Dongarra
’98]– FFTW [Frigo & Johnson ’98], SPIRAL [Pueschel, et al.,
’00], UHFFT [Mirkovic and Johnsson ’00]– MPI collective operations [Vadhiyar & Dongarra ’01]
• Code generation– FLAME [Gunnels & van de Geijn, ’01]– Sparse compilers: [Bik ’99], Bernoulli [Pingali, et al., ’97]– Generic programming: Blitz++ [Veldhuizen ’98], MTL
[Siek & Lumsdaine ’98], GMCL [Czarnecki, et al. ’98], …
• Sparse performance modeling– [Temam & Jalby ’92], [White & Saddayappan ’97],
[Navarro, et al., ’96], [Heras, et al., ’99], [Fraguela, et al., ’99], …
More Related Work
• Compiler analysis, models– CROPS [Carter, Ferrante, et al.]; Serial sparse tiling
[Strout ’01]– TUNE [Chatterjee, et al.]– Iterative compilation [O’Boyle, et al., ’98]– Broadway compiler [Guyer & Lin, ’99]– [Brewer ’95], ADAPT [Voss ’00]
• Sparse BLAS interfaces– BLAST Forum (Chapter 3)– NIST Sparse BLAS [Remington & Pozo ’94];
SparseLib++– SPARSKIT [Saad ’94]– Parallel Sparse BLAS [Fillipone, et al. ’96]
Context: Creating High-Performance Libraries
• Application performance dominated by a few computational kernels
• Today: Kernels hand-tuned by vendor or user• Performance tuning challenges
– Performance is a complicated function of kernel, architecture, compiler, and workload
– Tedious and time-consuming
• Successful automated approaches– Dense linear algebra: ATLAS/PHiPAC– Signal processing: FFTW/SPIRAL/UHFFT
Cache Blocked SpMV on LSI Matrix: Itanium
Sustainable Memory Bandwidth
Multiple Vector Performance: Pentium 4
Multiple Vector Performance: Itanium
Multiple Vector Performance: Pentium 4
Optimized AAT*x Performance: Ultra 2i
Optimized AAT*x Performance: Pentium 4
High Precision GEMV (XBLAS)
High Precision Algorithms (XBLAS)• Double-double (High precision word represented as pair of doubles)
– Many variations on these algorithms; we currently use Bailey’s• Exploiting Extra-wide Registers
– Suppose s(1) , … , s(n) have f-bit fractions, SUM has F>f bit fraction– Consider following algorithm for S = i=1,n s(i)
• Sort so that |s(1)| |s(2)| … |s(n)|• SUM = 0, for i = 1 to n SUM = SUM + s(i), end for, sum = SUM
– Theorem (D., Hida) Suppose F<2f (less than double precision)• If n 2F-f + 1, then error 1.5 ulps• If n = 2F-f + 2, then error 22f-F ulps (can be 1)• If n 2F-f + 3, then error can be arbitrary (S 0 but sum = 0 )
– Examples• s(i) double (f=53), SUM double extended (F=64)
– accurate if n 211 + 1 = 2049• Dot product of single precision x(i) and y(i)
– s(i) = x(i)*y(i) (f=2*24=48), SUM double extended (F=64) – accurate if n 216 + 1 = 65537
More Extra Slides
Awards• Best Paper, Intern. Conf. Parallel Processing, 2004
– “Performance models for evaluation and automatic performance tuning of symmetric sparse matrix-vector multiply”
• Best Student Paper, Intern. Conf. Supercomputing, Workshop on Performance Optimization via High-Level Languages and Libraries, 2003– Best Student Presentation too, to Richard Vuduc– “Automatic performance tuning and analysis of sparse triangular solve”
• Finalist, Best Student Paper, Supercomputing 2002– To Richard Vuduc– “Performance Optimization and Bounds for Sparse Matrix-vector Multiply”
• Best Presentation Prize, MICRO-33: 3rd ACM Workshop on Feedback-Directed Dynamic Optimization, 2000– To Richard Vuduc– “Statistical Modeling of Feedback Data in an Automatic Tuning System”
Accuracy of the Tuning Heuristics (4/4)
Can Match DGEMV PerformanceDGEMV
Fraction of Upper Bound Across Platforms
Achieved Performance and Machine Balance
• Machine balance [Callahan ’88; McCalpin ’95]– Balance = Peak Flop Rate / Bandwidth (flops /
double)– Lower is better (I.e. can hope to get higher fraction
of peak flop rate)
• Ideal balance for mat-vec: 2 flops / double– For SpMV, even less
Where Does the Time Go?
• Most time assigned to memory• Caches “disappear” when line sizes are equal
– Strictly increasing line sizes
1
memmem HitsHitsTimei
ii
Execution Time Breakdown: Matrix 40
Execution Time Breakdown (PAPI): Matrix 40
Speedups with Increasing Line Size
Summary: Performance Upper Bounds
• What is the best we can do for SpMV?– Limits to low-level tuning of blocked implementations– Refinements?
• What machines are good for SpMV?– Partial answer: balance characterization
• Architectural consequences?– Help to have strictly increasing line sizes
Evaluating algorithms and machines for SpMV
• Metrics– Speedups– Mflop/s (“fair” flops)– Fraction of peak
• Questions– Speedups are good, but what is “the best?”
• Independent of instruction scheduling, selection• Can SpMV be further improved or not?
– What machines are “good” for SpMV?
Statistical Models for Automatic Tuning
• Idea 1: Statistical criterion for stopping a search– A general search model
• Generate implementation• Measure performance• Repeat
– Stop when probability of being within of optimal falls below threshold
• Can estimate distribution on-line
• Idea 2: Statistical performance models– Problem: Choose 1 among m implementations at run-
time– Sample performance off-line, build statistical model
SpMV Historical Trends: Fraction of Peak
Motivation for Automatic Performance Tuning of SpMV
• Historical trends– Sparse matrix-vector multiply (SpMV): 10% of peak or
less
• Performance depends on machine, kernel, matrix– Matrix known at run-time– Best data structure + implementation can be surprising
• Our approach: empirical performance modeling and algorithm search
Sparse CALA Summary
• Optimizing one SpMV too limiting• Take k steps of Krylov subspace method
– GMRES, CG, Lanczos, Arnoldi– Assume matrix “well-partitioned,” with modest surface-to-
volume ratio– Parallel implementation
• Conventional: O(k log p) messages• CALA: O(log p) messages - optimal
– Serial implementation• Conventional: O(k) moves of data from slow to fast memory• CALA: O(1) moves of data – optimal
– Can incorporate some preconditioners• Need to be able to “compress” interactions between distant i, j• Hierarchical, semiseparable matrices …
– Lots of speed up possible (measured and modeled)• Price: some redundant computation
Motivation for Automatic Performance Tuning• Writing high performance software is hard
– Make programming easier while getting high speed
• Ideal: program in your favorite high level language (Matlab, Python, PETSc…) and get a high fraction of peak performance
• Reality: Best algorithm (and its implementation) can depend strongly on the problem, computer architecture, compiler,…– Best choice can depend on knowing a lot of applied
mathematics and computer science
• How much of this can we teach?• How much of this can we automate?
Examples of Automatic Performance Tuning (1)
• Dense BLAS– Sequential– PHiPAC (UCB), then ATLAS (UTK) (used in Matlab)– math-atlas.sourceforge.net/
• Fast Fourier Transform (FFT) & variations– Sequential and Parallel– FFTW (MIT)– www.fftw.org
• Digital Signal Processing– SPIRAL: www.spiral.net (CMU)
• Communication Collectives (UCB, UTK)• Rose (LLNL), Bernoulli (Cornell), Telescoping Languages
(Rice), …• More projects, conferences, government reports, …
Potential Impact on Applications: T3P
• Application: accelerator design [Ko] • 80% of time spent in SpMV• Relevant optimization techniques
– Symmetric storage– Register blocking
• On Single Processor Itanium 2– 1.68x speedup
• 532 Mflops, or 15% of 3.6 GFlop peak– 4.4x speedup with multiple (8) vectors
• 1380 Mflops, or 38% of peak
Examples of Automatic Performance Tuning (2)
• What do dense BLAS, FFTs, signal processing, MPI reductions have in common?– Can do the tuning off-line: once per architecture,
algorithm– Can take as much time as necessary (hours, a week…)– At run-time, algorithm choice may depend only on few
parameters• Matrix dimension, size of FFT, etc.
SpMV Performance (Matrix #2): Generation 2
Ultra 2i - 9% Ultra 3 - 5%
Pentium III-M - 15%Pentium III - 19%
63 Mflop/s
35 Mflop/s
109 Mflop/s
53 Mflop/s
96 Mflop/s
42 Mflop/s
120 Mflop/s
58 Mflop/s
SpMV Performance (Matrix #2): Generation 1
Power3 - 13% Power4 - 14%
Itanium 2 - 31%Itanium 1 - 7%
195 Mflop/s
100 Mflop/s
703 Mflop/s
469 Mflop/s
225 Mflop/s
103 Mflop/s
1.1 Gflop/s
276 Mflop/s
Taking advantage of block structure in SpMV
• Bottleneck is time to get matrix from memory• Don’t store each nonzero with index, instead
store each nonzero r-by-c block with index– Storage drops by up to 2x, if rc >> 1, all 32-bit
quantities– Time to fetch matrix from memory decreases
• Change both data structure and algorithm– Need to pick r and c– Need to change algorithm accordingly
• In example, is r=c=8 best choice?– Minimizes storage, so looks like a good idea…
Example: L2 Misses on Itanium 2
Misses measured using PAPI [Browne ’00]
Example: Sparse Triangular Factor
• Raefsky4 (structural problem) + SuperLU + colmmd
• N=19779, nnz=12.6 M
Dense trailing triangle: dim=2268, 20% of total nz
Can be as high as 90+%!1.8x over CSR
Cache Optimizations for AAT*x
• Cache-level: Interleave multiplication by A, AT
– Only fetch A from memory once
• Register-level: aiT to be rc block row, or diag
row
n
i
Tii
Tn
T
nT xaax
a
a
aaxAA1
1
1 )(
dot product“axpy”
… …
Example: Combining Optimizations (1/2)
• Register blocking, symmetry, multiple (k) vectors– Three low-level tuning parameters: r, c, v
v
kX
Y A
cr
+=
*
Example: Combining Optimizations (2/2)
• Register blocking, symmetry, and multiple vectors [Ben Lee @ UCB]– Symmetric, blocked, 1 vector
• Up to 2.6x over nonsymmetric, blocked, 1 vector
– Symmetric, blocked, k vectors• Up to 2.1x over nonsymmetric, blocked, k vecs.• Up to 7.3x over nonsymmetric, nonblocked, 1, vector
– Symmetric Storage: up to 64.7% savings
How the OSKI Tunes (Overview)
• At library build/install-time– Pre-generate and compile code variants into dynamic
libraries– Collect benchmark data
• Measures and records speed of possible sparse data structure and code variants on target architecture
– Installation process uses standard, portable GNU AutoTools• At run-time
– Library “tunes” using heuristic models• Models analyze user’s matrix & benchmark data to choose
optimized data structure and code– Non-trivial tuning cost: up to ~40 mat-vecs
• Library limits the time it spends tuning based on estimated workload
– provided by user or inferred by library• User may reduce cost by saving tuning results for application
on future runs with same or similar matrix
Optimizations in OSKI, so far
• Fully automatic heuristics for– Sparse matrix-vector multiply
• Register-level blocking• Register-level blocking + symmetry + multiple vectors• Cache-level blocking
– Sparse triangular solve with register-level blocking and “switch-to-dense” optimization
– Sparse ATA*x with register-level blocking• User may select other optimizations manually
– Diagonal storage optimizations, reordering, splitting; tiled matrix powers kernel (Ak*x)
– All available in dynamic libraries– Accessible via high-level embedded script language
• “Plug-in” extensibility– Very advanced users may write their own heuristics, create new
data structures/code variants and dynamically add them to the system
Performance Results• Measured
– Sequential/OOC speedup up to 3x
• Modeled – Sequential/multicore speedup up to 2.5x– Parallel/Petascale speedup up to 6.9x – Parallel/Grid speedup up to 22x
• See bebop.cs.berkeley.edu/#pubs