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pandoc:introduction-to-vsc:10_performance:10_performance [2017/10/19 05:46] – Pandoc Auto-commit pandocpandoc:introduction-to-vsc:10_performance:10_performance [2018/03/21 11:33] – Pandoc Auto-commit pandoc
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 ====== Going for efficiency - how to get out performance ====== ====== Going for efficiency - how to get out performance ======
  
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 |**Network** throughput (GB/s)             |2*4      |2*3.4     |2*2                     |0.2 / 0.0002         | |**Network** throughput (GB/s)             |2*4      |2*3.4     |2*2                     |0.2 / 0.0002         |
 |**Network** latency (µs)                  |1.4-1.8  |1.4-1.8   |1.4-1.8                 |2                    | |**Network** latency (µs)                  |1.4-1.8  |1.4-1.8   |1.4-1.8                 |2                    |
-|**Storage** throughput ("/global", GB/s)  |20       |15        |10                      |1                    |+|**Storage** throughput (“/global”, GB/s)  |20       |15        |10                      |1                    |
 |**Storage** latency (ms)                  |0.03     |1         |1                       |1000                 | |**Storage** latency (ms)                  |0.03     |1         |1                       |1000                 |
  
-"Operations": Floating point / 16 cores @3GHz; Values per node\\ +“Operations”: Floating point / 16 cores @3GHz; Values per node\\ 
-"Memory": Mixture of three cache levels and main memory; Values per node\\ +“Memory”: Mixture of three cache levels and main memory; Values per node\\ 
-"Network": Medium shared by all users (large jobs only); Values per node\\ +“Network”: Medium shared by all users (large jobs only); Values per node\\ 
-"Storage": Medium shared by all users; Values per application+“Storage”: Medium shared by all users; Values per application
  
 Which one is the **limiting factor** for a given code? Which one is the **limiting factor** for a given code?
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   * Large problem size => subalgorithm with highest exponent of complexity becomes dominant   * Large problem size => subalgorithm with highest exponent of complexity becomes dominant
     * **Measure** compute time, preferably with profiler     * **Measure** compute time, preferably with profiler
-    * Beware: **cache effects** can 'change' algorithmic complexity+    * Beware: **cache effects** can ‘change’ algorithmic complexity
   * Often: algorithmic complexity is well known and documented   * Often: algorithmic complexity is well known and documented
  
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 ===== Very important libraries ===== ===== Very important libraries =====
  
-  * **Level 3 BLAS** (="Matrix Multiplication")+  * **Level 3 BLAS** (=“Matrix Multiplication”)
     * O(n^3) operations with O(n^2) memory accesses     * O(n^3) operations with O(n^2) memory accesses
     * portable performance (often faster by a factor of 10)     * portable performance (often faster by a factor of 10)
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   * Naming schemes   * Naming schemes
-    * UPPER vs. lower case+    * UPPER vs. lower case
     * added underscores     * added underscores
  
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   * Call by value / call by reference   * Call by value / call by reference
   * Order of parameters: size of string parameters sometimes at the very end of parameter list   * Order of parameters: size of string parameters sometimes at the very end of parameter list
-  * I/O libraries +  * I/O libraries * Hint: use I/O of only one language, or * link with correct runtime libraries, or * link by calling compiler (e.g. gfortran) instead of linker (e.g. ld)
-    * Hint: use I/O of only one language, or +
-    * link with correct runtime libraries, or +
-    * link by calling compiler (e.g. gfortran) instead of linker (e.g. ld)+
   * Copy-in and/or copy-out: can affect performance   * Copy-in and/or copy-out: can affect performance
-  * Class/module information in objects ("*.o") only useful in one language+  * Class/module information in objects (“*.o“) only useful in one language
  
 ===== Interoperability (3) ===== ===== Interoperability (3) =====
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 ===== Memory optimization ===== ===== Memory optimization =====
  
-To keep CPUs busy, data has to be kept near the execution units, e.g. in **Level 1 Cache**+To keep CPUs busy, data has to be kept near the execution units, e.g. in **Level 1 Cache**
  
 Methods Methods
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 ===== Example: Blocking ===== ===== Example: Blocking =====
- 
-Three nested loops 
  
 <code> <code>
 +//Three nested loops
 N=10000; N=10000;
 for(i=0;i<N;i++) for(i=0;i<N;i++)
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     for(k=0;k<N;k++)     for(k=0;k<N;k++)
       C[i][j]+=A[i][k]*B[k][j];       C[i][j]+=A[i][k]*B[k][j];
 +//Large N => cache thrashing
 </code> </code>
-Large N => cache thrashing 
- 
- 
-Blocked access 
  
 <code> <code>
 +//Blocked access
 l=48; N=10000; l=48; N=10000;
 for(i=0;i<N;i+=l) for(i=0;i<N;i+=l)
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           for(kk=k;kk<min(k+l,N);kk++)           for(kk=k;kk<min(k+l,N);kk++)
             C[ii][jj]+=A[ii][kk]*B[kk][jj];             C[ii][jj]+=A[ii][kk]*B[kk][jj];
 +//Large N => reusing data in fast cache levels
 </code> </code>
-Large N => reusing data in fast cache levels+Adapt parameter l to size of cache – separately for each cache level
  
  
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   * 1000000 times 100B: 1000000 * 100B * 2GB/s + 1000000*2µs = 2050ms (blocking the file server)   * 1000000 times 100B: 1000000 * 100B * 2GB/s + 1000000*2µs = 2050ms (blocking the file server)
- 
-===== Parallelization ===== 
- 
-  * MPI<html><br></html> 20.-22.11.2017<html><br></html> VSC Training Course: Parallelization with MPI<html><br></html> Claudia Blaas-Schenner and Irene Reichl 
-  * OpenMP<html><br></html> 23.-24.11.2017<html><br></html> VSC Training Course: Shared memory parallelization with OpenMP<html><br></html> Lukas Einkemmer (lectures+practicals; Department of Mathematics, University of Innsbruck),<html><br></html> Claudia Blaas-Schenner and Irene Reichl (practicals only; VSC Team, TU Wien) 
- 
-http://vsc.ac.at 
  
 ===== Tools ===== ===== Tools =====
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   * VSC School Trainings upon request   * VSC School Trainings upon request
  
-https://wiki.vsc.ac.at/doku.php?id=doku:perf-report -- https://wiki.vsc.ac.at/doku.php?id=doku:forge+https://wiki.vsc.ac.at/doku.php?id=doku:perf-report – https://wiki.vsc.ac.at/doku.php?id=doku:forge
  
  • pandoc/introduction-to-vsc/10_performance/10_performance.txt
  • Last modified: 2020/10/20 08:09
  • by pandoc