Difference: CSX (1 vs. 5)

Revision 52012-04-27 - VasileiosKarakasis

Line: 1 to 1
 
META TOPICPARENT name="ActivitiesProjects"
The Compressed Sparse eXtended (CSX) format for sparse matrices is a sparse matrix format that seeks to minimize the memory footprint of the column index array of the typical Compressed Sparse Row (CSR) format by exploiting dense substructures inside the sparse matrix. Instead of storing a single index for every nonzero element of the sparse matrix, CSX stores a short description for each substructure found in the matrix (and selected for encoding). This technique can save significant amount of main memory storage and minimize the bandwidth requirements of the Sparse Matrix-Vector Multiplication (SpMV) kernel. Finally, the CSX format employes runtime code generation (using the LLVM compiler infrastructure) for emitting optimized SpMV routines for each encoded pattern.

Code

Changed:
<
<		A proof-of-concept implementation of CSX is available for download: csx-v0.1.tar.bz2.
>
>		A proof-of-concept implementation of CSX is available for download: libcsx_v0.2.tar.gz.
 Our code can also be found at https://github.com/cslab-ntua/csx. For questions related to the code (or CSX in general), please contact kkourt at cslab.ece.ntua.gr or bkk at cslab.ece.ntua.gr.

Publications

Changed:
<
<
  • K. Kourtis, V. Karakasis, G. Goumas, and N. Koziris, "CSX: An extended compression format for SpMV on shared memory systems," 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP'11) San Antonio, TX, USA, February 12-16, 2011 (pdf).
>
>
  • V. Karakasis, G. Goumas, K. Nikas, N. Koziris, J. Ruokolainen, and P. Råback, "Using State-of-the-Art Sparse Matrix Optimizations for Accelerating the Performance of Multiphysics Simulations" PARA 2012: Workshop on State-of-the-Art in Scientific and Parallel Computing. Helsinki, Finland, June 10–13, 2012 (pdf)
  • K. Kourtis, V. Karakasis, G. Goumas, and N. Koziris, "CSX: An extended compression format for SpMV on shared memory systems," 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP'11) San Antonio, TX, USA, February 12–16, 2011 (pdf).
 

Errata:

Changed:
<
<		The transformation for the anti-diagonal case shown in this paper is incorrect. The correct one is:
>
>		The transformation for the anti-diagonal case shown in the PPoPP'11 paper is incorrect. The correct one is:
 
i' = i + j - 1
j' = i, if i' <= ncols
Line: 22 to 23
 The transformation shown in the paper is the reverse transformation for the diagonal case.

Code links:

Changed:
<
<
  • anti-diagonal (i.e., reverse-diagonal) transformation: pnt_map_rD
  • diagonal reverse transformation: pnt_rmap_D
>
>
  • anti-diagonal (i.e., reverse-diagonal) transformation: pnt_map_rD
  • diagonal reverse transformation: pnt_rmap_D
  Reported by Xiuxia Zhang
Changed:
<
<
META FILEATTACHMENT attachment="csx-v0.1.tar.bz2" attr="" comment="CSX source (v0.1)" date="1297353066" name="csx-v0.1.tar.bz2" path="csx-v0.1.tar.bz2" size="63803" stream="csx-v0.1.tar.bz2" user="Main.KorniliosKourtis" version="1"
>
>
META FILEATTACHMENT attr="" autoattached="1" comment="CSX library v0.2" date="1335524448" name="libcsx_v0.2.tar.gz" path="libcsx_v0.2.tar.gz" size="145595" user="Main.VasileiosKarakasis" version="1"
META FILEATTACHMENT attr="" autoattached="1" comment="CSX source (v0.1)" date="1297353067" name="csx-v0.1.tar.bz2" path="csx-v0.1.tar.bz2" size="63803" user="Main.KorniliosKourtis" version="1"

Revision 42011-03-15 - KorniliosKourtis

Line: 1 to 1
 
META TOPICPARENT name="ActivitiesProjects"
The Compressed Sparse eXtended (CSX) format for sparse matrices is a sparse matrix format that seeks to minimize the memory footprint of the column index array of the typical Compressed Sparse Row (CSR) format by exploiting dense substructures inside the sparse matrix. Instead of storing a single index for every nonzero element of the sparse matrix, CSX stores a short description for each substructure found in the matrix (and selected for encoding). This technique can save significant amount of main memory storage and minimize the bandwidth requirements of the Sparse Matrix-Vector Multiplication (SpMV) kernel. Finally, the CSX format employes runtime code generation (using the LLVM compiler infrastructure) for emitting optimized SpMV routines for each encoded pattern.
Line: 11 to 11
 
  • K. Kourtis, V. Karakasis, G. Goumas, and N. Koziris, "CSX: An extended compression format for SpMV on shared memory systems," 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP'11) San Antonio, TX, USA, February 12-16, 2011 (pdf).
Added:
>
>

Errata:

 
Added:
>
>		The transformation for the anti-diagonal case shown in this paper is incorrect. The correct one is: 
i' = i + j - 1
j' = i, if i' <= ncols
j' = ncols + 1 - j, if i' > ncols
The transformation shown in the paper is the reverse transformation for the diagonal case.

Code links:

  • anti-diagonal (i.e., reverse-diagonal) transformation: pnt_map_rD
  • diagonal reverse transformation: pnt_rmap_D

Reported by Xiuxia Zhang

 
META FILEATTACHMENT attachment="csx-v0.1.tar.bz2" attr="" comment="CSX source (v0.1)" date="1297353066" name="csx-v0.1.tar.bz2" path="csx-v0.1.tar.bz2" size="63803" stream="csx-v0.1.tar.bz2" user="Main.KorniliosKourtis" version="1"

Revision 32011-02-10 - KorniliosKourtis

Line: 1 to 1
 
META TOPICPARENT name="ActivitiesProjects"
The Compressed Sparse eXtended (CSX) format for sparse matrices is a sparse matrix format that seeks to minimize the memory footprint of the column index array of the typical Compressed Sparse Row (CSR) format by exploiting dense substructures inside the sparse matrix. Instead of storing a single index for every nonzero element of the sparse matrix, CSX stores a short description for each substructure found in the matrix (and selected for encoding). This technique can save significant amount of main memory storage and minimize the bandwidth requirements of the Sparse Matrix-Vector Multiplication (SpMV) kernel. Finally, the CSX format employes runtime code generation (using the LLVM compiler infrastructure) for emitting optimized SpMV routines for each encoded pattern.
Added:
>
>

Code

 A proof-of-concept implementation of CSX is available for download: csx-v0.1.tar.bz2.
Changed:
<
<		Additionally, our code can also be found at https://github.com/cslab-ntua/csx. For questions related to the code (or CSX in general), please contact kkourt at cslab.ece.ntua.gr or bkk at cslab.ece.ntua.gr.
>
>		Our code can also be found at https://github.com/cslab-ntua/csx. For questions related to the code (or CSX in general), please contact kkourt at cslab.ece.ntua.gr or bkk at cslab.ece.ntua.gr.
 

Publications

Revision 22011-02-10 - KorniliosKourtis

Line: 1 to 1
 
META TOPICPARENT name="ActivitiesProjects"
The Compressed Sparse eXtended (CSX) format for sparse matrices is a sparse matrix format that seeks to minimize the memory footprint of the column index array of the typical Compressed Sparse Row (CSR) format by exploiting dense substructures inside the sparse matrix. Instead of storing a single index for every nonzero element of the sparse matrix, CSX stores a short description for each substructure found in the matrix (and selected for encoding). This technique can save significant amount of main memory storage and minimize the bandwidth requirements of the Sparse Matrix-Vector Multiplication (SpMV) kernel. Finally, the CSX format employes runtime code generation (using the LLVM compiler infrastructure) for emitting optimized SpMV routines for each encoded pattern.
Changed:
<
<		A proof-of-concept release of the CSX format can be found here.
>
>		A proof-of-concept implementation of CSX is available for download: csx-v0.1.tar.bz2. Additionally, our code can also be found at https://github.com/cslab-ntua/csx. For questions related to the code (or CSX in general), please contact kkourt at cslab.ece.ntua.gr or bkk at cslab.ece.ntua.gr.
 

Publications

  • K. Kourtis, V. Karakasis, G. Goumas, and N. Koziris, "CSX: An extended compression format for SpMV on shared memory systems," 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP'11) San Antonio, TX, USA, February 12-16, 2011 (pdf).
Changed:
<
<		-- VasileiosKarakasis - 10 Feb 2011
>
>

META FILEATTACHMENT attachment="csx-v0.1.tar.bz2" attr="" comment="CSX source (v0.1)" date="1297353066" name="csx-v0.1.tar.bz2" path="csx-v0.1.tar.bz2" size="63803" stream="csx-v0.1.tar.bz2" user="Main.KorniliosKourtis" version="1"

Revision 12011-02-10 - VasileiosKarakasis

Line: 1 to 1
Added:
>
>
META TOPICPARENT name="ActivitiesProjects"
The Compressed Sparse eXtended (CSX) format for sparse matrices is a sparse matrix format that seeks to minimize the memory footprint of the column index array of the typical Compressed Sparse Row (CSR) format by exploiting dense substructures inside the sparse matrix. Instead of storing a single index for every nonzero element of the sparse matrix, CSX stores a short description for each substructure found in the matrix (and selected for encoding). This technique can save significant amount of main memory storage and minimize the bandwidth requirements of the Sparse Matrix-Vector Multiplication (SpMV) kernel. Finally, the CSX format employes runtime code generation (using the LLVM compiler infrastructure) for emitting optimized SpMV routines for each encoded pattern.

A proof-of-concept release of the CSX format can be found here.

Publications

  • K. Kourtis, V. Karakasis, G. Goumas, and N. Koziris, "CSX: An extended compression format for SpMV on shared memory systems," 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP'11) San Antonio, TX, USA, February 12-16, 2011 (pdf).

-- VasileiosKarakasis - 10 Feb 2011

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