matsen · October 1, 2015 12:19 · Oct 1, 2015 · Sep 30, 2015 · Sep 30, 2015 · Sep 30, 2015
diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -38,7 +38,7 @@ NSF cash resulting in an excellent product. Their Agave API
 
 ### Parallel architectures require parallel algorithms.
 This is self-explanatory. In my field of Bayesian phylogenetics, for example,
-all algorithms in common use use Markov chain Monte Carlo, which is an
+all algorithms in common use utilize Markov chain Monte Carlo, which is an
 inherently serial algorithm. If we are to use large scale architecture we are
 going to need algorithms appropriate for that architecture.
 

diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -21,7 +21,7 @@ There is a clear antidote to this problem, which are software
 containers. These are lightweight virtual machines which can run on a
 variety of platforms. Docker is the most well known, and the community
 (including Docker) is coalescing under a standard. See
-https://www.opencontainers.org/ for details. Using containers, all
+<https://www.opencontainers.org/> for details. Using containers, all
 dependencies are cached and the pipeline can be run reliably into the
 future.
 
@@ -31,10 +31,10 @@ an entirely trivial technical consideration.
 
 ### iPlant collaborative is already doing a visionary job.
 As you are no doubt aware, the iPlant Collaborative environment
-(http://www.iplantcollaborative.org/) and the TACC under the leadership
+(<http://www.iplantcollaborative.org/>) and the TACC under the leadership
 of Dan Stanzione is a remarkable example of smart people given a chunk of
 NSF cash resulting in an excellent product. Their Agave API
-(http://agaveapi.co/) points the way to the future.
+(<http://agaveapi.co/>) points the way to the future.
 
 ### Parallel architectures require parallel algorithms.
 This is self-explanatory. In my field of Bayesian phylogenetics, for example,
@@ -43,10 +43,10 @@ inherently serial algorithm. If we are to use large scale architecture we are
 going to need algorithms appropriate for that architecture.
 
 ### Algorithms can give &gt; 100 fold improvement without additional infrastructure.
-As we have seen recently with the development of *kallisto* by Bray et al
-(http://arxiv.org/abs/1505.02710) algorithms can change problems from requiring
-a cluster to being quite do-able on a laptop. [Note: I understand that
-*kallisto* isn't the same as doing a full analysis with cufflinks, etc, but for
-many common applications it appears to do a fine job.] Thus, I hope that novel
-algorithm development for core computational problems will be part of any
-investment in computing infrastructure.
+As we have seen recently with the development of [*kallisto* by Bray et
+al](http://arxiv.org/abs/1505.02710) algorithms can change problems from
+requiring a cluster to being quite do-able on a laptop. [Note: I understand
+that running *kallisto* isn't the same as doing a full analysis with cufflinks,
+etc, but for many common applications it appears to do a fine job.] Thus,
+I hope that novel algorithm development for core computational problems will be
+part of any investment in computing infrastructure.
diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -40,7 +40,7 @@ NSF cash resulting in an excellent product. Their Agave API
 This is self-explanatory. In my field of Bayesian phylogenetics, for example,
 all algorithms in common use use Markov chain Monte Carlo, which is an
 inherently serial algorithm. If we are to use large scale architecture we are
-going to need corresponding algorithms.
+going to need algorithms appropriate for that architecture.
 
 ### Algorithms can give &gt; 100 fold improvement without additional infrastructure.
 As we have seen recently with the development of *kallisto* by Bray et al

diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -3,9 +3,9 @@
 ### We can piggyback on the coding development community.
 Many good things are happening in open source and industry, and we face many of
 the same issues that they do. For example, GitHub has provided enormous value
-to science, both through just filling a need we have and by direct engagement.
-It has gotten almost unbelievably popular in the computational life sciences.
-However, other tools such as continuous integration, for example by [Travis
+to science, both through filling a need and by direct engagement. It has gotten
+almost unbelievably popular in the computational life sciences. However, other
+tools such as continuous integration, for example by [Travis
 CI](http://travis-ci.org/), or containers, for example by
 [Docker](http://docker.com), have gotten less traction despite the
 contributions they could offer to the scientific community.
@@ -39,7 +39,8 @@ NSF cash resulting in an excellent product. Their Agave API
 ### Parallel architectures require parallel algorithms.
 This is self-explanatory. In my field of Bayesian phylogenetics, for example,
 all algorithms in common use use Markov chain Monte Carlo, which is an
-inherently serial algorithm.
+inherently serial algorithm. If we are to use large scale architecture we are
+going to need corresponding algorithms.
 
 ### Algorithms can give &gt; 100 fold improvement without additional infrastructure.
 As we have seen recently with the development of *kallisto* by Bray et al

diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -29,21 +29,18 @@ future.
 expansion of computing can run software containers.* This isn't
 an entirely trivial technical consideration.
 
-
 ### iPlant collaborative is already doing a visionary job.
 As you are no doubt aware, the iPlant Collaborative environment
 (http://www.iplantcollaborative.org/) and the TACC under the leadership
 of Dan Stanzione is a remarkable example of smart people given a chunk of
 NSF cash resulting in an excellent product. Their Agave API
 (http://agaveapi.co/) points the way to the future.
 
-
 ### Parallel architectures require parallel algorithms.
 This is self-explanatory. In my field of Bayesian phylogenetics, for example,
 all algorithms in common use use Markov chain Monte Carlo, which is an
 inherently serial algorithm.
 
-
 ### Algorithms can give &gt; 100 fold improvement without additional infrastructure.
 As we have seen recently with the development of *kallisto* by Bray et al
 (http://arxiv.org/abs/1505.02710) algorithms can change problems from requiring

diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -50,5 +50,5 @@ As we have seen recently with the development of *kallisto* by Bray et al
 a cluster to being quite do-able on a laptop. [Note: I understand that
 *kallisto* isn't the same as doing a full analysis with cufflinks, etc, but for
 many common applications it appears to do a fine job.] Thus, I hope that novel
-algorithm development for core computational problems will be part of any investment in
-computing infrastructure.
+algorithm development for core computational problems will be part of any
+investment in computing infrastructure.
diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -50,5 +50,5 @@ As we have seen recently with the development of *kallisto* by Bray et al
 a cluster to being quite do-able on a laptop. [Note: I understand that
 *kallisto* isn't the same as doing a full analysis with cufflinks, etc, but for
 many common applications it appears to do a fine job.] Thus, I hope that novel
-algorithm development for core problems will be part of any investment in
+algorithm development for core computational problems will be part of any investment in
 computing infrastructure.
diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -1,13 +1,16 @@
 ## Response to [*Science Drivers Requiring Capable Exascale High Performance Computing* RFI](http://grants.nih.gov/grants/guide/notice-files/NOT-GM-15-122.html)
 
 ### We can piggyback on the coding development community.
-Many good things are happening in open source and industry, and we face many of the same issues that they do.
-For example, GitHub has provided enormous value to science, both through just filling a need we have and by direct engagement. 
+Many good things are happening in open source and industry, and we face many of
+the same issues that they do. For example, GitHub has provided enormous value
+to science, both through just filling a need we have and by direct engagement.
 It has gotten almost unbelievably popular in the computational life sciences.
-However, other tools such as continuous integration, for example by [Travis CI](http://travis-ci.org/), or containers, for example by [Docker](http://docker.com), have gotten less traction despite the contributions they could offer to the scientific community.
+However, other tools such as continuous integration, for example by [Travis
+CI](http://travis-ci.org/), or containers, for example by
+[Docker](http://docker.com), have gotten less traction despite the
+contributions they could offer to the scientific community.
 
 ### We need a strategy to fight bit-rot.
-
 "Bit-rot" refers to software/pipelines that become unusable because the
 underlying dependencies have changed. Sometimes the old versions
 disappear completely, meaning that old pipelines cannot be reconstructed
@@ -28,7 +31,6 @@ an entirely trivial technical consideration.
 
 
 ### iPlant collaborative is already doing a visionary job.
-
 As you are no doubt aware, the iPlant Collaborative environment
 (http://www.iplantcollaborative.org/) and the TACC under the leadership
 of Dan Stanzione is a remarkable example of smart people given a chunk of
@@ -43,7 +45,6 @@ inherently serial algorithm.
 
 
 ### Algorithms can give &gt; 100 fold improvement without additional infrastructure.
-
 As we have seen recently with the development of *kallisto* by Bray et al
 (http://arxiv.org/abs/1505.02710) algorithms can change problems from requiring
 a cluster to being quite do-able on a laptop. [Note: I understand that

diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -31,7 +31,7 @@ an entirely trivial technical consideration.
 
 As you are no doubt aware, the iPlant Collaborative environment
 (http://www.iplantcollaborative.org/) and the TACC under the leadership
-of Dan Stanzione is a remarkble example of smart people given a chunk of
+of Dan Stanzione is a remarkable example of smart people given a chunk of
 NSF cash resulting in an excellent product. Their Agave API
 (http://agaveapi.co/) points the way to the future.
 

diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -24,7 +24,7 @@ future.
 
 *For NIH computational strategies, we need to make sure that any proposed
 expansion of computing can run software containers.* This isn't
-an entirely trivial consideration.
+an entirely trivial technical consideration.
 
 
 ### iPlant collaborative is already doing a visionary job.

diff --git a/response-to-exascale-hpc-rfi.md b/response-to-exascale-hpc-rfi.md
@@ -1,5 +1,11 @@
 ## Response to [*Science Drivers Requiring Capable Exascale High Performance Computing* RFI](http://grants.nih.gov/grants/guide/notice-files/NOT-GM-15-122.html)
 
+### We can piggyback on the coding development community.
+Many good things are happening in open source and industry, and we face many of the same issues that they do.
+For example, GitHub has provided enormous value to science, both through just filling a need we have and by direct engagement. 
+It has gotten almost unbelievably popular in the computational life sciences.
+However, other tools such as continuous integration, for example by [Travis CI](http://travis-ci.org/), or containers, for example by [Docker](http://docker.com), have gotten less traction despite the contributions they could offer to the scientific community.
+
 ### We need a strategy to fight bit-rot.
 
 "Bit-rot" refers to software/pipelines that become unusable because the

diff --git a/test.md → response-to-exascale-hpc-rfi.md b/test.md → response-to-exascale-hpc-rfi.md
diff --git a/test.md b/test.md
@@ -1,3 +1,5 @@
+## Response to [*Science Drivers Requiring Capable Exascale High Performance Computing* RFI](http://grants.nih.gov/grants/guide/notice-files/NOT-GM-15-122.html)
+
 ### We need a strategy to fight bit-rot.
 
 "Bit-rot" refers to software/pipelines that become unusable because the

diff --git a/test.md b/test.md
@@ -0,0 +1,45 @@
+### We need a strategy to fight bit-rot.
+
+"Bit-rot" refers to software/pipelines that become unusable because the
+underlying dependencies have changed. Sometimes the old versions
+disappear completely, meaning that old pipelines cannot be reconstructed
+without digging through the internet archive. Reproducibility is
+fundamental to science, and thus this problem is acute.
+
+There is a clear antidote to this problem, which are software
+containers. These are lightweight virtual machines which can run on a
+variety of platforms. Docker is the most well known, and the community
+(including Docker) is coalescing under a standard. See
+https://www.opencontainers.org/ for details. Using containers, all
+dependencies are cached and the pipeline can be run reliably into the
+future.
+
+*For NIH computational strategies, we need to make sure that any proposed
+expansion of computing can run software containers.* This isn't
+an entirely trivial consideration.
+
+
+### iPlant collaborative is already doing a visionary job.
+
+As you are no doubt aware, the iPlant Collaborative environment
+(http://www.iplantcollaborative.org/) and the TACC under the leadership
+of Dan Stanzione is a remarkble example of smart people given a chunk of
+NSF cash resulting in an excellent product. Their Agave API
+(http://agaveapi.co/) points the way to the future.
+
+
+### Parallel architectures require parallel algorithms.
+This is self-explanatory. In my field of Bayesian phylogenetics, for example,
+all algorithms in common use use Markov chain Monte Carlo, which is an
+inherently serial algorithm.
+
+
+### Algorithms can give &gt; 100 fold improvement without additional infrastructure.
+
+As we have seen recently with the development of *kallisto* by Bray et al
+(http://arxiv.org/abs/1505.02710) algorithms can change problems from requiring
+a cluster to being quite do-able on a laptop. [Note: I understand that
+*kallisto* isn't the same as doing a full analysis with cufflinks, etc, but for
+many common applications it appears to do a fine job.] Thus, I hope that novel
+algorithm development for core problems will be part of any investment in
+computing infrastructure.
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