Implementation and evaluation of data-compressionalgorithms for irregular-grid iterative methodson the PEZY-SC processor
Naoki Yoshifuji (Fixstars)
Ryo Sakamoto (Fixstars, present: PEZY Computing)
Keigo Nitadori (RIKEN AICS)
Jun Makino (Department of Planetology, Kobe University)
IA3 2016 Sixth Workshop on Irregular Applications: Architectures and Algorithms (SC16 Workshop) @ Salt Lake City, UT
Talk Summary
HPCG was implemented on PEZY-SC on ZettaScaler system
Single-chip performance of SpMV is 11.6 GFLOPS, which is 93% of the theretical limit determined by the memory bandwidth
Simple and fast matrix compression were applied to SpMV and tested
Data+Index table-based compression improved performance by a factor of 2.8
Data compression can be very powerful way to improve performance of unstructured grid calculation
Introduction
Contents
Introduction
PEZY-SC and ZettaScaler
HPCG on PEZY
SpMV with compression
Conclusion
To solve linear equations
Many real problems (e.g. FEM or other CAE problems) requires to solve large linear equations:
Ax=b
A is large sparse and irregular matrix in most cases of real problems
Iterative methods are suited
Multiplication of sparse matrix and vector (SpMV) is the most time-consuming process
SpMV is slow on modern HPC systems
Example: Top 3 of June 2016 HPCG results
Rank
Computer
Rpeak
HPCG
HPCG/Rpeak
1
MilkyWay-2
54.9
0.58
1.1%
2
K computer
11.3
0.55
4.9%
3
Sunway TaihuLight
125.4
0.37
0.3%
Modern computer's memory bandwidth is too small against (arithmetic) instruction throughput for SpMV
Our propposal
A simple data compression and decompression for sparse matrix
↓
Reduce memory access
↓
Improve performance
PEZY-SC and ZettaScaler
Contents
Introduction
PEZY-SC and ZettaScaler
HPCG on PEZY
SpMV with compression
Conclusion
Why PEZY-SC?
MIMD processor →easy implementation
Byte per Flop is very small →easy to check compression efficiency
HPCG on PEZY
Contents
Introduction
PEZY-SC and ZettaScaler
HPCG on PEZY
SpMV with compression
Conclusion
Why HPCG?
A standard benchmark for iterative method (MGCG)
Its model is close to real problem (3D diffusion eq.)
Result of HPCG on PEZY
Achieved 168.06 GFLOPS with 8 nodes (32 PEZY-SCs)
Fraction of time consuming
SpMV analysis
93% of the theoretical limit
Achieved
11.6 GFLOPS
Theoretical by memory bandwidth
12.5 GFLOPS
SpMV with compression
Contents
Introduction
PEZY-SC and ZettaScaler
HPCG on PEZY
SpMV with compression
Conclusion
Matrix in HPCG
Illustration of the matrix in HPCG
2 same values
Several same patterns for non-diagonal position
Matrix in real application
Same coefficients
If the physical characteristics of the material is uniform
Same relative index pattern
If we can use effectively regular grids for the bulk of the material
Table-based sparse matrix compression
Data table compression
extract same value of matrix elements
Index table compression
extract same pattern of column number
SpMV result on HPCG matrix
Compression
Achieved GFLOPS
Theoretical GFLOPS
ratio (achieved/theretical)
None
11.6
12.5
x1.0 / x1.0
Data table
15.9
34.8
x1.4 / x2.8
Data+Index table
32.4
326.0
x2.8 / x26.1
NOTE: theoretical estimate ignores input vector random access
Conclusion
Contents
Introduction
PEZY-SC and ZettaScaler
HPCG on PEZY
SpMV with compression
Conclusion
Conclusion
HPCG was implemented on PEZY-SC on ZettaScaler system
Single-chip performance of SpMV is 11.6 GFLOPS, which is 93% of the theretical limit determined by the memory bandwidth
Simple and fast matrix compression were applied to SpMV and tested
Data+Index table-based compression improved performance by a factor of 2.8
Compression technique will improvesolver for linear equationsin CAE application and otherson the current and future HPC system!
Acknowledgment
The authors would like to thank people in PEZY Computing/ExaScaler for their invaluable help in solving many problems we encountered while porting and tuning HPCG.
Part of the research covered in this paper research was funded by MEXT’s program for the Development and Improvement for the Next Generation Ultra High-Speed Computer System,
under its Subsidies for Operating the Specific Advanced Large Research Facilities.
Implementation and evaluation of data-compression algorithms for irregular-grid iterative methods on the PEZY-SC processor
Naoki Yoshifuji (Fixstars)
Ryo Sakamoto (Fixstars, present: PEZY Computing)
Keigo Nitadori (RIKEN AICS)
Jun Makino (Department of Planetology, Kobe University)
2016-11-13
IA3 2016 Sixth Workshop on Irregular Applications: Architectures and Algorithms (SC16 Workshop) @ Salt Lake City, UT
