Viterbi decoder on STI CELL processor Michal Blaek - - PowerPoint PPT Presentation
Viterbi decoder on STI CELL processor Michal Blaek - - PowerPoint PPT Presentation
IBM - CVUT Student Research Projects Viterbi decoder on STI CELL processor Michal Blaek (blazem2@fel.cvut.cz) Viterbi algorithm introduction Finds the most likely state trajectory given the Works in several steps HMM model and
Viterbi algorithm introduction
- Works in several steps
- Finds the most likely state trajectory given the
HMM model and observation’s sequence.
IBM - CVUT Student Research Projects 2
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* 1 1 * 1 * 1 * 1 1 1 1 t 1 1
2,...,1
- T
1,
- T
for t ng backtracki . 4 max arg max :
- n
terminati . 3 max arg max N j 1 T, t 2 for : recursion 2. ) ( N i 1 for : tion initializa 1.
+ + ≤ ≤ ≤ ≤ − ≤ ≤ − ≤ ≤
⋅ = = = = ⋅ = ⋅ ⋅ = ≤ ≤ ≤ ≤ ⋅ = ≤ ≤
t t t T N i T T N i ij t N i t t j ij t N i i i
q q i q i P a i j O b a i j O b i ψ δ δ δ ψ δ δ π δ
ALF introduction
(Accelerated Library Framework)
- Programming environment
for data and task parallel applications.
- Supports the MPMD
(multiple-program-multiple- data) programming model.
- Optimal tasks scheduling
scheme based on tasks dependencies.
- Two components: host
runtime X accelerator runtime
- Input + output +
parameters = work block
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Program realization
- Algorithm is divide in four separated TASKs creating a simple
pipeline.
- Between particular neighbouring TASKs is defined a time
dependency for running in right order.
- TASKs which using ALF are computed in parallel on 1-6 SPEs.
- Program uses “Host data partitioning” method for partition data
across the accelerators (SPEs).
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Conclusion and results
- Working correctly.
- The most difficult part (recursion) is not completed yet
with ALF, for checking results is for now working on PPU(using SIMD) only.
- No optimalizations and tune.
- Orientation computation time comparison on the same
data set (HMM-500x500x500, seq-500): – This: 10,5 sec – Old (1SPE, libspe2, SIMD): 18,3 sec
IBM - CVUT Student Research Projects 5