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[[File:Summary.PNG]] | [[File:Summary.PNG]] | ||
− | |||
+ | [[File:Function_timmings.PNG]] | ||
+ | |||
+ | the image above shows the timings for each function | ||
+ | |||
+ | matmul_0 - represents serial version | ||
+ | |||
+ | matmul_1 - represents serial version with reverse logic | ||
+ | |||
+ | matmul_2 - uses cilk_for | ||
+ | |||
+ | matmul_3 - uses cilk_for and reducer hyperboject | ||
+ | |||
+ | matmul_4 - uses cilk_for, reducer and vectorization | ||
Line 47: | Line 59: | ||
====matmul_0 (Serial)==== | ====matmul_0 (Serial)==== | ||
+ | <pre> | ||
+ | double matmul_0(const double* a, const double* b, double* c, int n) { | ||
+ | for (int i = 0; i < n; i++) { | ||
+ | for (int j = 0; j < n; j++) { | ||
+ | double sum = 0.0; | ||
+ | for (int k = 0; k < n; k++) | ||
+ | sum += a[i * n + k] * b[k * n + j]; | ||
+ | c[i * n + j] = sum; | ||
+ | } | ||
+ | } | ||
+ | double diag = 0.0; | ||
+ | for (int i = 0; i < n; i++) | ||
+ | diag += c[i * n + i]; | ||
+ | return diag; | ||
+ | } | ||
+ | </pre> | ||
[[File:Conc-01.png]] | [[File:Conc-01.png]] | ||
Line 53: | Line 81: | ||
====matmul_1 (Serial with j-k loops reversed)==== | ====matmul_1 (Serial with j-k loops reversed)==== | ||
+ | <pre> | ||
+ | double matmul_1(const double* a, const double* b, double* c, int n) { | ||
+ | |||
+ | for (int i = 0; i < n; i++) { | ||
+ | for (int k = 0; k < n; k++) { | ||
+ | double sum = 0.0; | ||
+ | for (int j = 0; j < n; j++) | ||
+ | sum += a[i * n + k] * b[k * n + j]; | ||
+ | c[i * n + k] = sum; | ||
+ | } | ||
+ | } | ||
+ | double diag = 0.0; | ||
+ | for (int i = 0; i < n; i++) | ||
+ | diag += c[i * n + i]; | ||
+ | return diag; | ||
+ | } | ||
+ | </pre> | ||
[[File:Conc-11.png]] | [[File:Conc-11.png]] | ||
Line 59: | Line 104: | ||
====matmul_2 (Cilk Plus with cilk_for)==== | ====matmul_2 (Cilk Plus with cilk_for)==== | ||
+ | <pre> | ||
+ | double matmul_2(const double* a, const double* b, double* c, int n) { | ||
+ | |||
+ | cilk_for (int i = 0; i < n; i++) { | ||
+ | cilk_for (int j = 0; j < n; j++) { | ||
+ | double sum = 0.0; | ||
+ | for(int k = 0; k < n; k++) { | ||
+ | sum += a[i * n + k] * b[k * n + j]; | ||
+ | } | ||
+ | c[i * n + j] = sum; | ||
+ | } | ||
+ | } | ||
+ | |||
+ | double diag = 0.0; | ||
+ | for (int i = 0; i < n; i++) | ||
+ | diag += c[i * n + i]; | ||
+ | return diag; | ||
+ | } | ||
+ | </pre> | ||
[[File:Conc-21.png]] | [[File:Conc-21.png]] | ||
Line 65: | Line 129: | ||
====matmul_3 (+array notation, reducer)==== | ====matmul_3 (+array notation, reducer)==== | ||
+ | <pre> | ||
+ | double matmul_3(const double* a, const double* b, double* c, int n) { | ||
+ | |||
+ | cilk_for(int i = 0; i < n; i++) { | ||
+ | cilk_for(int j = 0; j < n; j++) { | ||
+ | double sum = 0.0; | ||
+ | for (int k = 0; k < n; k++) { | ||
+ | sum += a[i * n + k] * b[k * n + j]; | ||
+ | } | ||
+ | c[i * n + j] = sum; | ||
+ | } | ||
+ | } | ||
+ | |||
+ | cilk::reducer_opadd <double> diag(0.0); | ||
+ | cilk_for(int i = 0; i < n; i++) { | ||
+ | diag += c[i * n + i]; | ||
+ | } | ||
+ | return diag.get_value(); | ||
+ | } | ||
+ | </pre> | ||
[[File:Conc-31.png]] | [[File:Conc-31.png]] | ||
Line 71: | Line 155: | ||
====matmul_4 (+vectorization)==== | ====matmul_4 (+vectorization)==== | ||
+ | <pre> | ||
+ | double matmul_4(const double* a, const double* b, double* c, int n) { | ||
+ | |||
+ | cilk_for(int i = 0; i < n; i++) { | ||
+ | cilk_for(int j = 0; j < n; j++) { | ||
+ | double sum = 0.0; | ||
+ | #pragma simd | ||
+ | for (int k = 0; k < n; k++) { | ||
+ | sum += a[i * n + k] * b[k * n + j]; | ||
+ | } | ||
+ | c[i * n + j] = sum; | ||
+ | } | ||
+ | } | ||
+ | |||
+ | cilk::reducer_opadd <double> diag(0.0); | ||
+ | cilk_for(int i = 0; i < n; i++) { | ||
+ | diag += c[i * n + i]; | ||
+ | } | ||
+ | return diag.get_value(); | ||
+ | } | ||
+ | </pre> | ||
[[File:Conc-41.png]] | [[File:Conc-41.png]] | ||
Line 85: | Line 190: | ||
===Locks & Waits=== | ===Locks & Waits=== | ||
− | + | * Best for locating causes of low concurrency, such as heavily used locks and large critical sections. | |
− | + | * Locks are when threads are waiting too long on synchronization objects. | |
+ | * Uses user-mode sampling and tracing collection to identify processes. | ||
+ | * This analysis shows time spent waiting on synchronizations. | ||
− | |||
− | + | [[File:Lock1.png]] | |
+ | [[File:Lock2.png]] | ||
− | + | [[File:Lock3.png]] | |
− | |||
==references== | ==references== | ||
Line 111: | Line 217: | ||
https://software.intel.com/en-us/vtune-amplifier-help-locks-and-waits-analysis | https://software.intel.com/en-us/vtune-amplifier-help-locks-and-waits-analysis | ||
− | https://software.intel.com/en-us/ | + | https://software.intel.com/en-us/vtuneampxe_hotspots_win_c |
− | |||
− | |||
− | https://software.intel.com/en-us/ | + | https://software.intel.com/en-us/vtuneampxe_locks_win_c |
Latest revision as of 11:53, 5 January 2018
Contents
Group Members
Intel Parallel Studio vTune Amplifier
What is VTune Amplifier?
- A tool created by Intel to provide performance analysis on software.
- Offers both a GUI and command-line version for both Windows and Linux
- GUI only for OSX
- Basic features available on both Intel and AMD processors, but advanced features only for Intel
How to use it?
- Available as a standalone unit or part of the following packages:
- Intel Parallel Studio XE Cluster Edition and Professional Edition
- Intel Media Server Studio Professional Edition
- Intel System Studio
Can be run on a local machine
Hotspots
Basic hotspot analysis
We used our workshop 6 as an example to demonstrate this particular aspect of Intel Vtune Amplifer
the image above shows the timings for each function
matmul_0 - represents serial version
matmul_1 - represents serial version with reverse logic
matmul_2 - uses cilk_for
matmul_3 - uses cilk_for and reducer hyperboject
matmul_4 - uses cilk_for, reducer and vectorization
Parallelism
Concurrency
- Best for visualizing thread parallelism on available cores, finding areas with high or low concurrency, and identifying serial bottlenecks in your code
- Provides information on how many threads were running at each moment during application execution
- Includes threads which are currently running or ready to run and therefore are not waiting at a defined waiting or blocking API
- Also shows CPU time while the hotspot was executing and estimates its effectiveness either by CPU usage or by Threads Concurrency
Results of Concurrency tests on Workshop 6
I ran the Concurrency test on each of the functions in Workshop 6. I isolated each function by commenting out all others, then ran them 1 by 1. This was to get an idea of how they perform on their own. Finally I ran them all together to see how the program runs overall.
matmul_0 (Serial)
double matmul_0(const double* a, const double* b, double* c, int n) { for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { double sum = 0.0; for (int k = 0; k < n; k++) sum += a[i * n + k] * b[k * n + j]; c[i * n + j] = sum; } } double diag = 0.0; for (int i = 0; i < n; i++) diag += c[i * n + i]; return diag; }
matmul_1 (Serial with j-k loops reversed)
double matmul_1(const double* a, const double* b, double* c, int n) { for (int i = 0; i < n; i++) { for (int k = 0; k < n; k++) { double sum = 0.0; for (int j = 0; j < n; j++) sum += a[i * n + k] * b[k * n + j]; c[i * n + k] = sum; } } double diag = 0.0; for (int i = 0; i < n; i++) diag += c[i * n + i]; return diag; }
matmul_2 (Cilk Plus with cilk_for)
double matmul_2(const double* a, const double* b, double* c, int n) { cilk_for (int i = 0; i < n; i++) { cilk_for (int j = 0; j < n; j++) { double sum = 0.0; for(int k = 0; k < n; k++) { sum += a[i * n + k] * b[k * n + j]; } c[i * n + j] = sum; } } double diag = 0.0; for (int i = 0; i < n; i++) diag += c[i * n + i]; return diag; }
matmul_3 (+array notation, reducer)
double matmul_3(const double* a, const double* b, double* c, int n) { cilk_for(int i = 0; i < n; i++) { cilk_for(int j = 0; j < n; j++) { double sum = 0.0; for (int k = 0; k < n; k++) { sum += a[i * n + k] * b[k * n + j]; } c[i * n + j] = sum; } } cilk::reducer_opadd <double> diag(0.0); cilk_for(int i = 0; i < n; i++) { diag += c[i * n + i]; } return diag.get_value(); }
matmul_4 (+vectorization)
double matmul_4(const double* a, const double* b, double* c, int n) { cilk_for(int i = 0; i < n; i++) { cilk_for(int j = 0; j < n; j++) { double sum = 0.0; #pragma simd for (int k = 0; k < n; k++) { sum += a[i * n + k] * b[k * n + j]; } c[i * n + j] = sum; } } cilk::reducer_opadd <double> diag(0.0); cilk_for(int i = 0; i < n; i++) { diag += c[i * n + i]; } return diag.get_value(); }
Final test with all functions
Locks & Waits
- Best for locating causes of low concurrency, such as heavily used locks and large critical sections.
- Locks are when threads are waiting too long on synchronization objects.
- Uses user-mode sampling and tracing collection to identify processes.
- This analysis shows time spent waiting on synchronizations.
references
https://en.wikipedia.org/wiki/VTune
https://software.intel.com/en-us/get-started-with-vtune
https://software.intel.com/en-us/vtune-amplifier-help-analysis-types
https://software.intel.com/en-us/vtune-amplifier-help-basic-hotspots-analysis
https://software.intel.com/en-us/vtune-amplifier-help-advanced-hotspots-analysis
https://software.intel.com/en-us/vtune-amplifier-help-concurrency-analysis
https://software.intel.com/en-us/vtune-amplifier-help-locks-and-waits-analysis