Download Particle Mesh Ewald(PME) method

Accelerating HMMER Search on GPUs using
Hybrid Task and Data Parallelism
Narayan Ganesan1, Roger Chamberlain2, Jeremy Buhler2 and
Michela Taufer1
Computer and Info. Sciences Dept, University of Delaware.1
Computer Science and Engineering, Washington University in
St. Louis2
Motivation
• Dataset size is growing for many applications rapidly
• Problem sizes grow as the product of the data sizes
•
•
•
•
•
 E.g., Genome sequence alignment, protein motif finding
Number of problems also grows as the product of number of
available data
Many sequentially dependent algorithms are executed serially
Processor speed has hit a brick wall (~3.5-4.00GHz in 2003) and
serial evaluation is just not feasible for large data applications
 Need for parallelism is greater than ever
Parallel hardware is ubiquitous
 GPU, Multi-core, SIMD, Hybrid, MIMD, FPGA
Efforts must be spent on parallelizing algorithms and
applications for these hardware
Application: Protein Motif Finding
• Proteins are synthesized by biological Processes that can be described by HMMs
Class A
Protein Synthesis
.
.
.
Class N
Protein Synthesis
...PAQVEMYKFLLRISQLNRD...
... CHTEARGLEGVCIDPKK...
... DGEACNSPYLDWRKDTEQ...
... CTPPSLAACTPPTS...
... LTITNLMKSLGFKPKPKKI...
... DELAAMVRDYLKKTPEF...
• Each class is described by short characteristic sequences or motifs
• Each class or “generator” is described by a Hidden Markov model
• Protein Motif Finding answers the questions:
 Given a sequence what class does it belong to?
 Given a sequence and a HMM what is the probability that the sequence
belongs to that class?
Protein Motif Finding
• Protein motif finding is very similar to signal source identification
• A protein sequence may contain multiple motifs:
..ACGFTDFWAPSLTHLTIKNL..
• Motifs in the sequence are sometimes modified by addition and deletion
•
•
•
of random amino acids
Occurrence of motifs can be modeled by Profile Hidden Markov models
Viterbi algorithm is used to “decode” a given protein sequence against a
model
The result is the probability that the sequence belongs to the class
Protein Synthesis
...PAQVEMYKFLLRISQLNRD...
... CHTEARGLEGVCIDPKK...
... DGEACNSPYLDWRKDTEQ...
HMMER Search - Protein Motif Finding
Sample Paths
S
N
B
D4
D2
D3
I1
I2
I3
I4
M1
M2
M3
M4
M5
E
C
T
J
....ACGFTDFWAPSLTHLTIKNL....
....ACGFTDFWAPSLTHLTIKNL....
....ACGFTDFWAPSALTHLTIKNL....
....ACGFTDFWAPSAGLTHLTIKNL...
....ACGFTDFWAPSAGL-HLTIKNL...
HMMER Search – Recurrence Equations
D2
S
N
B
D3
D4
I1
I2
I3
I4
M1
M2
M3
M4
J
VD
VI
VM
M5
E
C
T
HMMER Search- Protein Motif Finding
1
1
Model
j
m
Insert:
S
e
q
u
e
n
c
e
XE
Match:
Delete:
i
VD (i, j ) max( VD (i, j  1)  ci , di )
L
• Dependence on XE imposes a row major order computation
• Delete state costs impose a serial dependency on previous element in the
row
• Harder to parallelize by conventional means
HMMER Search – Protein Motif Finding
• Delete costs impose sequential dependency
VD (i,3k )  max( VD (i,2k )  c3k , d3k )
VD (i, k )  max( VD (i,1)  ck , d k )
1
m
row i
VD (i,2k )  max( VD (i, k )  c2 k , d 2 k )
VD (i, m)  max( VD (i,3k )  cm , d m )
• Parallelize the row calculations
• The recurrence for VD is parallelizable by blocking strategy
NVIDIA-CUDA Programming Interface
Threads
Thread Blocks
...
...
Multiprocessor 1
Multiprocessor 2
Shared Memory
Registers
Processor 1
Registers
Processor 2
…
Shared Memory
Registers
Processor M
Registers
Instruction
Unit
Processor 1
Registers
Processor 2
…
Registers
Processor M
Constant Cache
Constant Cache
Texture Cache
Texture Cache
Global Memory
Instruction
Unit
Traditional Task Parallelism for GPUs
• Different GPU threads work on
independent tasks
• Time taken for different tasks
vary according to the nature of
the job
 Different sequences of
different lengths take
different time to complete
 Load imbalance issues are
possible
 Total time depends on location
of the longest sequences in the
database
Seq i
Thread
Block 1
Seq i+M
Thread
Block 2
Thread
Block P
Hybrid data and task parallelism
• We extract parallelism out of data
•
•
dependency
 Multiple GPU threads cooperate
to work on the same task
By dividing the database into roughly
equal chunks, we naturally solve any
load imbalance problems
This technique works for uniform
recurrence equations
 Ubiquitous in computational
biology including local sequence
alignment, multiple sequence
alignment, motif finding
Seq i
Thread
Block 1
Seq i+M
Thread
Block 2
Thread
Block P
GPU Implementation
row i
1
m
• Multiple threads cooperate by partitioning a single sequence and
•
•
•
working on different partitions
Working set is one row of the DP matrix, 507x3, 32-bit integers
 Model of size 507 has 507x9 transition probabilities and
507x40 emission probabilities stored as short integers
Model data is read in a coalesced form into the shared memory
Working set is stored and updated within shared memory
Performance and Results
• We compared our implementation on 1 and 4 GPUs versus the mpi-gpu
HMMER for the same dataset
3 HMM Sizes:
• 128
• 256
• 507
NCBI NR Protein Database:
• 5.5GB in Size
• 10.5 Million Protein
Sequences
Conclusions and Future Work
• Our GPU implementation of HMMER is:
 5-8x faster than GPU-HMMER search implementation for the same
data set
 100x faster than the CPU implementation
• Future work: Phylogenetic Motif Identification
 Identify common genetic motifs among several groups of organisms
 Representative of evolutionary relatedness among different (typically
1000s) species
 Closely related to multiple sequence alignment problem via Hidden
Markov Models
 Computationally intensive for which faster motif finding is absolutely
necessary