Key Features

Identifying Specialization

CBI is currently limited to identifying two forms of specialization:

1. Different source files are compiled for different platforms;

2. Lines of code within source files are guarded by C preprocessor macros.

Although limited, this functionality is sufficient to support analysis of many HPC codes, and CBI has been tested on C, C++, CUDA and some Fortran code bases.

Computing Specialization Metrics

CBI computes code divergence and platform coverage by building a specialization tree, like the one shown below:

CBI can then walk and evaluate this tree for different platform definitions, to produce a report providing a breakdown of how many lines of code are shared between different platform sets.

---------------------------------------------
                      Platform Set LOC % LOC
---------------------------------------------
                                {}   2  4.88
                           {GPU 1}   1  2.44
                           {GPU 2}   1  2.44
                           {CPU 2}   1  2.44
                           {CPU 1}   1  2.44
                            {FPGA}  14 34.15
                    {GPU 2, GPU 1}   6 14.63
                    {CPU 1, CPU 2}   6 14.63
{FPGA, CPU 1, GPU 2, GPU 1, CPU 2}   9 21.95
---------------------------------------------
Code Divergence: 0.55
Coverage (%): 95.12
Avg. Coverage (%): 42.44
Total SLOC: 41

For more information about these metrics, see here.

Hierarchical Clustering

Since code divergence is constructed from pair-wise distances, CBI can also produce a pair-wise distance matrix, showing the ratio of platform-specific code to code used by both platforms.

Distance Matrix
-----------------------------------
      FPGA CPU 1 GPU 2 GPU 1 CPU 2
-----------------------------------
 FPGA 0.00  0.70  0.70  0.70  0.70
CPU 1 0.70  0.00  0.61  0.61  0.12
GPU 2 0.70  0.61  0.00  0.12  0.61
GPU 1 0.70  0.61  0.12  0.00  0.61
CPU 2 0.70  0.12  0.61  0.61  0.00
-----------------------------------

These distances can also be used to produce a dendrogram, showing the result of hierarchical clustering by platform similarity.

Visualizing Platform Coverage

To assist developers in identifying exactly which parts of their code are specialized and for which platforms, CBI can produce an annotated tree showing the amount of specialization within each file.

Legend:
A: cpu
B: gpu

Columns:
[Platforms | SLOC | Coverage (%) | Avg. Coverage (%)]

[AB | 1.0k |   2.59 |   1.83] o /path/to/sample-code-base/src/
[-- | 1.0k |   0.00 |   0.00] |-- unused.cpp
[AB |   13 | 100.00 |  92.31] |-- main.cpp
[A- |    7 | 100.00 |  50.00] |-o cpu/
[A- |    7 | 100.00 |  50.00] | \-- foo.cpp
[-B |    7 | 100.00 |  50.00] \-o gpu/
[-B |    7 | 100.00 |  50.00]   \-- foo.cpp