I was looking for DNA assemblers that work on GPU. I found only this paper http://www.cs.gmu.edu/~tr-admin/pape...-TR-2011-1.pdf - GPU Euler. But I was not satisfied with the concept and results provided in the paper. It works by finding the whole Euler tour without any graph transformation and error correction, still it is getting results comparable to well established assemblers like Euler SR. Like max-length of 40,000 , N-50 value 8000, etc. No other assembler works by finding the whole Euler tour then how this paper is mentioning such good results. Does anyone have read or worked with this paper?
Seqanswers Leaderboard Ad
Collapse
Announcement
Collapse
No announcement yet.
X
-
I would say GPU is a no-go for genome assembly. We looked at various options for doing genome assembly in GPUs last year, and could not make the algorithms scale well. Genome assembly programs need very large memory bandwidth, and it is not possible to scale the programs well in the GPUs, whose greatest benefit is access to many 'parallel' processors. Late last year, I attended BGI's booth at HPC conference (Salt Lake City) and saw a number of GPU solutions being presented for various bioinformatics problems, but the genome assembly program did not seem to give any performance boost. At present our group is working on implementing a genome assembler in FPGA, where we can get the performance boost.
I will forward your question to BGI's Ruibang, who can probably shed more light on the current status.
-
According to the tech report, 90% of total time goes to "I/O". If I understand correctly, this "I/O" phase, unusually, includes k-mer counting and is done purely with CPU. K-mer counting is one of the slowest and most memory hungry steps in the construction of de Bruijn graph. If we cannot parallelize this step with GPU, we will not get much speed up.
In addition, the reported assembly speed is slower than what I would expect with velvet. I think velvet can usually get the results in a minute or so given 20X error-free data for a ~2Mbp genome. That is in par with GPU-Euler.
In all, I think the tech report does not prove that a GPU-based de Bruijn assembler is much better than CPU-based ones.
Comment
-
Thanks Samanta and lh3. I'm not closing the possibility of implementing an ultra-fast assembler on GPU, but the reduction nature of genome assembly problem constrained it from scaling well on GPU. For the latest GPU model nVidia GTX Titan, which has 2600+ cores but only ~300G memory bandwidth, every core will only get ~100MB/s memory bandwidth, not mentioning the optimal can only be achieve by coalesced memory access, which is almost impossible to be fulfilled no matter using DBG, String Graph or Greedy. Another problem is that GPU has only limited amount of on-board memory (3G-12G), swapping between host memory and GPU memory is possible but ultimately slow.
Differently, the problem of alignment is mainly "mapping" problem in MapReduce scheme, which makes it suitable for GPU or other HPC accelerator like FPGA and MIC. Plenty investigations have been done: "SOAP3-dp" (http://arxiv.org/abs/1302.5507) and "CUSHAW2-GPU" (http://cushaw2.sourceforge.net/homepage.htm#latest) has achieved more than 10x acceleration to CPU aligners, the most important, much higher sensitivity and accuracy in opening large gaps provide much more computational power.
BTW, frankly speaking, CPU assemblers, say SOAPdenovo2 and ALLPATH-LG, still have a large space to be improved. Samanta has a very good discussion on the hash function used in assemblers (http://homolog.us/blogs). A question is that, why we have to use standard, general hash functions in assembler? The only feature assemblers require the hash functions to have is the evenness, why shall we care that much about avalanche test.
Comment
-
Here are the links.
Few days back, a reader asked us in Twitter, whether de Bruijn graph-based assemblers could save and reload de Bruijn graphs from one another. Short Twitter answer was no. Long answer follows here.
We have been going through various web-based resources on high-quality hash functions and made a startling discovery. None of the good websites was maintained by the members of computer science departments at top universities or even second-rank universities. Based on our highly anecdotal evidence, computer science professors stopped thinking about hash functions many decades back. That seemed puzzling, because in the world where it matters, research on hash function still attracts big money.
Comment
-
Originally posted by davispeter View Posthmmmm. Thanks for reply. But what about the Euler approach. In the paper (that I mentioned) the Euler approach is implemented parallely. Does that mean Euler approach is not good?
-mark
Comment
-
Latest Articles
Collapse
-
by seqadmin
The first FDA-approved CRISPR-based therapy marked the transition of therapeutic gene editing from a dream to reality1. CRISPR technologies have streamlined gene editing, and CRISPR screens have become an important approach for identifying genes involved in disease processes2. This technique introduces targeted mutations across numerous genes, enabling large-scale identification of gene functions, interactions, and pathways3. Identifying the full range...-
Channel: Articles
08-27-2024, 04:44 AM -
-
by seqadmin
Sequencing mRNA provides a snapshot of cellular activity, allowing researchers to study the dynamics of cellular processes, compare gene expression across different tissue types, and gain insights into the mechanisms of complex diseases. “mRNA’s central role in the dogma of molecular biology makes it a logical and relevant focus for transcriptomic studies,” stated Sebastian Aguilar Pierlé, Ph.D., Application Development Lead at Inorevia. “One of the major hurdles for...-
Channel: Articles
08-07-2024, 12:11 PM -
ad_right_rmr
Collapse
News
Collapse
Topics | Statistics | Last Post | ||
---|---|---|---|---|
Started by seqadmin, 08-27-2024, 04:40 AM
|
0 responses
16 views
0 likes
|
Last Post
by seqadmin
08-27-2024, 04:40 AM
|
||
New Single-Molecule Sequencing Platform Introduces Advanced Features for High-Throughput Genomics
by seqadmin
Started by seqadmin, 08-22-2024, 05:00 AM
|
0 responses
293 views
0 likes
|
Last Post
by seqadmin
08-22-2024, 05:00 AM
|
||
Started by seqadmin, 08-21-2024, 10:49 AM
|
0 responses
135 views
0 likes
|
Last Post
by seqadmin
08-21-2024, 10:49 AM
|
||
Started by seqadmin, 08-19-2024, 05:12 AM
|
0 responses
124 views
0 likes
|
Last Post
by seqadmin
08-19-2024, 05:12 AM
|
Comment