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This is what I have really been looking forward to. I theorycrafted a more ideal system for the genetics work a former employer was doing, but didn't get to bui

This is what I have really been looking forward to. I theorycrafted a more ideal system for the genetics work a former employer was doing, but didn't get to build it until after I had left there. A quad 16 core opteron system for a total of 64 cores (for physics calculations in comsol). I think that there is more potential use for high actual core count servers than many people realize, so I can't wait to build one. (for my purposes these days is as an game server in a colo, one of my projects is a multiplayer UE4 game) At the previous job where I built the 64-core system, I even emailed the AMD marketing department to see if we could do some PR campaign together, but I think it was too soon before the Naples drop, because I never got a response. Here's to hoping supermicro does a 4 cpu board for this... 124 cores would be amazing. (But I'll take 64 naples cores as long as it gets rid of the bugs and issues I found with the opterons).

deepnotderp 10y ago
Out of curiosity, I thought that genetics was the domain of gpus?
- CreRecombinase 10y ago
  It's quite rare to find GPUs being used in genetics.
  - moh_maya 10y ago
    //OT:: Your user name: a fan of the cre-lox system, or the enzyme itself? Cool uid! In my past life, I've used flp/frt & cre/lox; and studied mismatch repair enzymes. And topoisomerases.. :)
  - noir_lord 10y ago
    Is that because the workloads are fundamentally unsuitable for current GPU architectures or because no one has took a good stab at it yet? I know very little about computation genetics/biology but it sounds interesting.
    - moh_maya 10y ago
      I don't think it is because no one has tried it as much as the fact that the workloads need the cpu architecture / are not easily parallizable (as far as I understand). Comp bio in genetics is largely sequence alignment & search, which is still largely CPU / memory bound; but I don't understand programming enough to speculate if development in algorithms will allow GPUs to be used because the problem itself is not parallelizable. I think of it as the difference between a super computer & a cluster.. (More than a decade ago, I struggled to / barely succeeded in building a Beowulf cluster; I am just amazed at how far both the hardware & the software tools have come..) In other areas of comp bio though, GPUs I think are finding use. Protein folding, molecular dynamics. Also, with STORM & such: super resolution microscopy? I think increasingly, gpus will become important. Also, whole cell simulations?
      - timeu 10y ago
        What you wrote about super computer vs cluster is quite right. Recently I attended a HPC meeting where we were the only DevOps of an HPC for a biological institute and most of the other people were from physics & chemistry. They usually don't consider the biology workloads as High Performance Computing but as big resource/data computing. The physics & chemistry guys run simulation using hundred thousands of cores and are mostly CPU bound. They use MPI and their nodes typically have not more than 64 GB and they consider 120 GB memory usage as a lot. Biologist on the other hand hardly use MPI because they can just parallelize the workload on the data level (i.e. sample or chromosome) and run them independently on each node. For that reason also high memory NUMA machines from SGI can relatively often be found. You are also right that some of the comp bio areas (CryoEM, protein folding, molecular dynamics) are well suited for GPUs
      - noir_lord 10y ago
        Thank you for your response, it was extremely interesting. One of the nice things about HN is you get to look outside your own bubble (I mostly do Line of Business/SME stuff so this stuff isn't just outside my wheelhouse it's on the other side of the ocean).
    - timeu 10y ago
- kannanvijayan 10y ago
  I did sequence-based bioinformatics back around 2006 or so. Very few of the operations used GPU. Things may have changed since I was working there, but the work at the time wasn't suited for a GPU architecture. Initial step was sequence cleanup, which is a hidden markov model executed over a collection of sequences of varying length, so hard to parallelize. Sequence annotation is embarassingly parallel on a per-library basis (each sequence can be annotated independently of the other), but the computational work is fuzzy string matching, which is once again hard to GPU-ize. Another major computational job was contig assembly, which is somewhat parallelizable (pairwise sequence comparisons), but once again involves fuzzy string matching so not GPU-izable. So that's just sequence genetics. Don't know if GPUs are used in other areas. Lots of cores, lots of threads, and lots of main memory. That was the key.
  - arca_vorago 10y ago
    "Lots of cores, lots of threads, and lots of main memory. That was the key." Very much this. Which is why I ended up theorycrafting that the AMD many core CPU's would be so useful.
    - deepnotderp 10y ago
      Hmmm, isn't deep learning starting to pick up for genetics? No idea if it is actually is, but everyone in DL seems to be talking about it, I thought I'd ask someone actually in bioinformatics :)
      - moh_maya 10y ago
        I suspect DL will have a limited to modest role in the actual search / alignment part, and a lot more to do with the analysis part. This includes medical diagnosis, identifying regulatory patterns based on high throughput expression data, such stuff. Not necessarily in comp. genetics / sequencing.. / the DNA stuff..
      - deleted 10y ago
        [deleted]
      - 10y ago
- arca_vorago 10y ago
  I think there is plenty of room for GPU usage in bioinformatics, but there are some barriers that prevent it from gaining prevelance, such as cost vs cpus, and lack of updates (example, gpu-blast is still 1.1, blast is 1.2).
- yread 10y ago
  For most of the really time consuming steps the speedup isn't spectacular, 1.6x is not worth the effort http://ce-publications.et.tudelft.nl/publications/1520_gpuaccelerated_bwamem_genomic_mapping_algorithm_using_ada.pdf http://ce-publications.et.tudelft.nl/publications/1520_gpuac...
sorenjan 10y ago
Wouldn't Xeon phi be a good choice for that kind of usage?

deepnotderp 10y ago

Out of curiosity, I thought that genetics was the domain of gpus?

CreRecombinase 10y ago
It's quite rare to find GPUs being used in genetics.
- moh_maya 10y ago
  //OT:: Your user name: a fan of the cre-lox system, or the enzyme itself? Cool uid! In my past life, I've used flp/frt & cre/lox; and studied mismatch repair enzymes. And topoisomerases.. :)
- noir_lord 10y ago
  Is that because the workloads are fundamentally unsuitable for current GPU architectures or because no one has took a good stab at it yet? I know very little about computation genetics/biology but it sounds interesting.
  - moh_maya 10y ago
    I don't think it is because no one has tried it as much as the fact that the workloads need the cpu architecture / are not easily parallizable (as far as I understand). Comp bio in genetics is largely sequence alignment & search, which is still largely CPU / memory bound; but I don't understand programming enough to speculate if development in algorithms will allow GPUs to be used because the problem itself is not parallelizable. I think of it as the difference between a super computer & a cluster.. (More than a decade ago, I struggled to / barely succeeded in building a Beowulf cluster; I am just amazed at how far both the hardware & the software tools have come..) In other areas of comp bio though, GPUs I think are finding use. Protein folding, molecular dynamics. Also, with STORM & such: super resolution microscopy? I think increasingly, gpus will become important. Also, whole cell simulations?
    - timeu 10y ago
      What you wrote about super computer vs cluster is quite right. Recently I attended a HPC meeting where we were the only DevOps of an HPC for a biological institute and most of the other people were from physics & chemistry. They usually don't consider the biology workloads as High Performance Computing but as big resource/data computing. The physics & chemistry guys run simulation using hundred thousands of cores and are mostly CPU bound. They use MPI and their nodes typically have not more than 64 GB and they consider 120 GB memory usage as a lot. Biologist on the other hand hardly use MPI because they can just parallelize the workload on the data level (i.e. sample or chromosome) and run them independently on each node. For that reason also high memory NUMA machines from SGI can relatively often be found. You are also right that some of the comp bio areas (CryoEM, protein folding, molecular dynamics) are well suited for GPUs
    - noir_lord 10y ago
      Thank you for your response, it was extremely interesting. One of the nice things about HN is you get to look outside your own bubble (I mostly do Line of Business/SME stuff so this stuff isn't just outside my wheelhouse it's on the other side of the ocean).
  - timeu 10y ago
kannanvijayan 10y ago
I did sequence-based bioinformatics back around 2006 or so. Very few of the operations used GPU. Things may have changed since I was working there, but the work at the time wasn't suited for a GPU architecture. Initial step was sequence cleanup, which is a hidden markov model executed over a collection of sequences of varying length, so hard to parallelize. Sequence annotation is embarassingly parallel on a per-library basis (each sequence can be annotated independently of the other), but the computational work is fuzzy string matching, which is once again hard to GPU-ize. Another major computational job was contig assembly, which is somewhat parallelizable (pairwise sequence comparisons), but once again involves fuzzy string matching so not GPU-izable. So that's just sequence genetics. Don't know if GPUs are used in other areas. Lots of cores, lots of threads, and lots of main memory. That was the key.
- arca_vorago 10y ago
  "Lots of cores, lots of threads, and lots of main memory. That was the key." Very much this. Which is why I ended up theorycrafting that the AMD many core CPU's would be so useful.
  - deepnotderp 10y ago
    Hmmm, isn't deep learning starting to pick up for genetics? No idea if it is actually is, but everyone in DL seems to be talking about it, I thought I'd ask someone actually in bioinformatics :)
    - moh_maya 10y ago
      I suspect DL will have a limited to modest role in the actual search / alignment part, and a lot more to do with the analysis part. This includes medical diagnosis, identifying regulatory patterns based on high throughput expression data, such stuff. Not necessarily in comp. genetics / sequencing.. / the DNA stuff..
    - deleted 10y ago
      [deleted]
    - 10y ago
arca_vorago 10y ago
I think there is plenty of room for GPU usage in bioinformatics, but there are some barriers that prevent it from gaining prevelance, such as cost vs cpus, and lack of updates (example, gpu-blast is still 1.1, blast is 1.2).
yread 10y ago
For most of the really time consuming steps the speedup isn't spectacular, 1.6x is not worth the effort http://ce-publications.et.tudelft.nl/publications/1520_gpuaccelerated_bwamem_genomic_mapping_algorithm_using_ada.pdf http://ce-publications.et.tudelft.nl/publications/1520_gpuac...

sorenjan 10y ago

Wouldn't Xeon phi be a good choice for that kind of usage?