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Developer manual

Source code layout

OpenBLAS/  
├── benchmark                  Benchmark codes for BLAS
├── cmake                      CMakefiles
├── ctest                      Test codes for CBLAS interfaces
├── driver                     Implemented in C
│   ├── level2
│   ├── level3
│   ├── mapper
│   └── others                 Memory management, threading, etc
├── exports                    Generate shared library
├── interface                  Implement BLAS and CBLAS interfaces (calling driver or kernel)
│   ├── lapack
│   └── netlib
├── kernel                     Optimized assembly kernels for CPU architectures
│   ├── alpha                  Original GotoBLAS kernels for DEC Alpha
│   ├── arm                    ARMV5,V6,V7 kernels (including generic C codes used by other architectures)
│   ├── arm64                  ARMV8
│   ├── generic                General kernel codes written in plain C, parts used by many architectures.
│   ├── ia64                   Original GotoBLAS kernels for Intel Itanium
│   ├── mips
│   ├── mips64
│   ├── power
|   ├── riscv64
|   ├── simd                   Common code for Universal Intrinsics, used by some x86_64 and arm64 kernels
│   ├── sparc
│   ├── x86
│   ├── x86_64
│   └── zarch   
├── lapack                      Optimized LAPACK codes (replacing those in regular LAPACK)
│   ├── getf2
│   ├── getrf
│   ├── getrs
│   ├── laswp
│   ├── lauu2
│   ├── lauum
│   ├── potf2
│   ├── potrf
│   ├── trti2
│   ├── trtri
│   └── trtrs
├── lapack-netlib               LAPACK codes from netlib reference implementation
├── reference                   BLAS Fortran reference implementation (unused)
├── relapack                    Elmar Peise's recursive LAPACK (implemented on top of regular LAPACK)
├── test                        Test codes for BLAS
└── utest                       Regression test

A call tree for dgemm looks as follows: 

interface/gemm.c
        │
driver/level3/level3.c
        │
gemm assembly kernels at kernel/

To find the kernel currently used for a particular supported CPU, please check the corresponding kernel/$(ARCH)/KERNEL.$(CPU) file.

Here is an example for kernel/x86_64/KERNEL.HASWELL: 

...
DTRMMKERNEL    =  dtrmm_kernel_4x8_haswell.c
DGEMMKERNEL    =  dgemm_kernel_4x8_haswell.S
...
According to the above KERNEL.HASWELL, OpenBLAS Haswell dgemm kernel file is dgemm_kernel_4x8_haswell.S.

Optimizing GEMM for a given hardware

Read the Goto paper to understand the algorithm

Goto, Kazushige; van de Geijn, Robert A. (2008). "Anatomy of High-Performance Matrix Multiplication". ACM Transactions on Mathematical Software 34 (3): Article 12

(The above link is available only to ACM members, but this and many related papers is also available on the pages of van de Geijn's FLAME project)

The driver/level3/level3.c is the implementation of Goto's algorithm. Meanwhile, you can look at kernel/generic/gemmkernel_2x2.c, which is a naive 2x2 register blocking gemm kernel in C. Then:

  • Write optimized assembly kernels. Consider instruction pipeline, available registers, memory/cache access.
  • Tune cache block sizes (Mc, Kc, and Nc)

Note that not all of the CPU-specific parameters in param.h are actively used in algorithms. DNUMOPT only appears as a scale factor in profiling output of the level3 syrk interface code, while its counterpart SNUMOPT (aliased as NUMOPT in common.h) is not used anywhere at all.

SYMV_P is only used in the generic kernels for the symv and chemv/zhemv functions - at least some of those are usually overridden by CPU-specific implementations, so if you start by cloning the existing implementation for a related CPU you need to check its KERNEL file to see if tuning SYMV_P would have any effect at all.

GEMV_UNROLL is only used by some older x86-64 kernels, so not all sections in param.h define it. Similarly, not all of the CPU parameters like L2 or L3 cache sizes are necessarily used in current kernels for a given model - by all indications the CPU identification code was imported from some other project originally.

Running OpenBLAS tests

We use tests for Netlib BLAS, CBLAS, and LAPACK. In addition, we use OpenBLAS-specific regression tests. They can be run with Make:

  • make -C test for BLAS tests
  • make -C ctest for CBLAS tests
  • make -C utest for OpenBLAS regression tests
  • make lapack-test for LAPACK tests

We also use the BLAS-Tester tests for regression testing. It is basically the ATLAS test suite adapted for building with OpenBLAS.

The project makes use of several Continuous Integration (CI) services conveniently interfaced with GitHub to automatically run tests on a number of platforms and build configurations.

Also note that the test suites included with "numerically heavy" projects like Julia, NumPy, SciPy, Octave or QuantumEspresso can be used for regression testing, when those projects are built such that they use OpenBLAS.

Benchmarking

A number of benchmarking methods are used by OpenBLAS:

  • Several simple C benchmarks for performance testing individual BLAS functions are available in the benchmark folder. They can be run locally through the Makefile in that directory. And the benchmark/scripts subdirectory contains similar benchmarks that use OpenBLAS via NumPy, SciPy, Octave and R.
  • On pull requests, a representative set of functions is tested for performance regressions with Codspeed; results can be viewed at https://codspeed.io/OpenMathLib/OpenBLAS.
  • The OpenMathLib/BLAS-Benchmarks repository contains an Airspeed Velocity-based benchmark suite which is run on several CPU architectures in cron jobs. Results are published to a dashboard: http://www.openmathlib.org/BLAS-Benchmarks/.

Benchmarking code for BLAS libraries, and specific performance analysis results, can be found in a number of places. For example:

Adding autodetection support for a new revision or variant of a supported CPU

Especially relevant for x86-64, a new CPU model may be a "refresh" (die shrink and/or different number of cores) within an existing model family without significant changes to its instruction set (e.g., Intel Skylake and Kaby Lake still are fundamentally the same architecture as Haswell, low end Goldmont etc. are Nehalem). In this case, compilation with the appropriate older TARGET will already lead to a satisfactory build.

To achieve autodetection of the new model, its CPUID (or an equivalent identifier) needs to be added in the cpuid_<architecture>.c relevant for its general architecture, with the returned name for the new type set appropriately. For x86, which has the most complex cpuid file, there are two functions that need to be edited: get_cpuname() to return, e.g., CPUTYPE_HASWELL and get_corename() for the (broader) core family returning, e.g., CORE_HASWELL.1

For architectures where DYNAMIC_ARCH builds are supported, a similar but simpler code section for the corresponding runtime detection of the CPU exists in driver/others/dynamic.c (for x86), and driver/others/dynamic_<arch>.c for other architectures. Note that for x86 the CPUID is compared after splitting it into its family, extended family, model and extended model parts, so the single decimal number returned by Linux in /proc/cpuinfo for the model has to be converted back to hexadecimal before splitting into its constituent digits. For example, 142 == 8E translates to extended model 8, model 14.

Adding dedicated support for a new CPU model

Usually it will be possible to start from an existing model, clone its KERNEL configuration file to the new name to use for this TARGET and eventually replace individual kernels with versions better suited for peculiarities of the new CPU model. In addition, it is necessary to add (or clone at first) the corresponding section of GEMM_UNROLL parameters in the top-level param.h, and possibly to add definitions such as USE_TRMM (governing whether TRMM functions use the respective GEMM kernel or a separate source file) to the Makefiles (and CMakeLists.txt) in the kernel directory. The new CPU name needs to be added to TargetList.txt, and the CPU auto-detection code used by the getarch helper program - contained in the cpuid_<architecture>.c file amended to include the CPUID (or equivalent) information processing required (see preceding section).

Adding support for an entirely new architecture

This endeavour is best started by cloning the entire support structure for 32-bit ARM, and within that the ARMv5 CPU in particular, as this is implemented through plain C kernels only. An example providing a convenient "shopping list" can be seen in pull request #1526.

  1. This information ends up in the Makefile.conf and config.h files generated by getarch. Failure to set either will typically lead to a missing definition of the GEMM_UNROLL parameters later in the build, as getarch_2nd will be unable to find a matching parameter section in param.h. ↩