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Details: - Added section titled "Reproduction" to both Performance.md and PerformanceSmall.md that briefly nudges the motivated reader in the right direction if he/she wishes to run the same performance benchmarks used to produce the graphs shown in those documents. Thanks to Dave Love for making this suggestion.
341 lines
16 KiB
Markdown
341 lines
16 KiB
Markdown
# Contents
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* **[Contents](PerformanceSmall.md#contents)**
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* **[Introduction](PerformanceSmall.md#introduction)**
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* **[General information](PerformanceSmall.md#general-information)**
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* **[Interpretation](PerformanceSmall.md#interpretation)**
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* **[Reproduction](PerformanceSmall.md#reproduction)**
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* **[Level-3 performance](PerformanceSmall.md#level-3-performance)**
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* **[Kaby Lake](PerformanceSmall.md#kaby-lake)**
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* **[Experiment details](PerformanceSmall.md#kaby-lake-experiment-details)**
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* **[Results](PerformanceSmall.md#kaby-lake-results)**
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* **[Haswell](PerformanceSmall.md#haswell)**
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* **[Experiment details](PerformanceSmall.md#haswell-experiment-details)**
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* **[Results](PerformanceSmall.md#haswell-results)**
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* **[Epyc](PerformanceSmall.md#epyc)**
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* **[Experiment details](PerformanceSmall.md#epyc-experiment-details)**
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* **[Results](PerformanceSmall.md#epyc-results)**
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* **[Feedback](PerformanceSmall.md#feedback)**
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# Introduction
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This document showcases performance results for the level-3 `gemm` operation
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on small matrices with BLIS and BLAS for select hardware architectures.
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# General information
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Generally speaking, for level-3 operations on small matrices, we publish
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two "panels" for each type of hardware, one that reflects performance on
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row-stored matrices and another for column-stored matrices.
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Each panel will consist of a 4x7 grid of graphs, with each row representing
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a different transposition case (`nn`, `nt`, `tn`, `tt`)
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complex) and each column representing a different shape scenario, usually
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with one or two matrix dimensions bound to a fixed size for all problem
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sizes tested.
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Each of the 28 graphs within a panel will contain an x-axis that reports
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problem size, with one, two, or all three matrix dimensions equal to the
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problem size (e.g. _m_ = 6; _n_ = _k_, also encoded as `m6npkp`).
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The y-axis will report in units GFLOPS (billions of floating-point operations
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per second) on a single core.
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It's also worth pointing out that the top of each graph (e.g. the maximum
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y-axis value depicted) _always_ corresponds to the theoretical peak performance
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under the conditions associated with that graph.
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Theoretical peak performance, in units of GFLOPS, is calculated as the
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product of:
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1. the maximum sustainable clock rate in GHz; and
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2. the maximum number of floating-point operations (flops) that can be
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executed per cycle.
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Note that the maximum sustainable clock rate may change depending on the
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conditions.
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For example, on some systems the maximum clock rate is higher when only one
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core is active (e.g. single-threaded performance) versus when all cores are
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active (e.g. multithreaded performance).
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The maximum number of flops executable per cycle (per core) is generally
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computed as the product of:
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1. the maximum number of fused multiply-add (FMA) vector instructions that
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can be issued per cycle (per core);
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2. the maximum number of elements that can be stored within a single vector
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register (for the datatype in question); and
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3. 2.0, since an FMA instruction fuses two operations (a multiply and an add).
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The problem size range, represented on the x-axis, is sampled in
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increments of 4 up to 800 for the cases where one or two dimensions is small
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(and constant)
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and up to 400 in the case where all dimensions (e.g. _m_, _n_, and _k_) are
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bound to the problem size (i.e., square matrices).
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Note that the constant small matrix dimensions were chosen to be _very_
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small--in the neighborhood of 8--intentionally to showcase what happens when
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at least one of the matrices is abnormally "skinny." Typically, organizations
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and individuals only publish performance with square matrices, which can miss
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the problem sizes of interest to many applications. Here, in addition to square
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matrices (shown in the seventh column), we also show six other scenarios where
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one or two `gemm` dimensions (of _m,_ _n_, and _k_) is small.
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The legend in each graph contains two entries for BLIS, corresponding to the
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two black lines, one solid and one dotted. The dotted line, **"BLIS conv"**,
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represents the conventional implementation that targets large matrices. This
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was the only implementation available in BLIS prior to the addition to the
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small/skinny matrix support. The solid line, **"BLIS sup"**, makes use of the
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new small/skinny matrix implementation for certain small problems. Whenever
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these results differ by any significant amount (beyond noise), it denotes a
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problem size for which BLIS employed the new small/skinny implementation.
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Put another way, **the delta between these two lines represents the performance
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improvement between BLIS's previous status quo and the new regime.**
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Finally, each point along each curve represents the best of three trials.
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# Interpretation
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In general, the the curves associated with higher-performing implementations
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will appear higher in the graphs than lower-performing implementations.
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Ideally, an implementation will climb in performance (as a function of problem
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size) as quickly as possible and asymptotically approach some high fraction of
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peak performance.
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When corresponding with us, via email or when opening an
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[issue](https://github.com/flame/blis/issues) on github, we kindly ask that
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you specify as closely as possible (though a range is fine) your problem
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size of interest so that we can better assist you.
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# Reproduction
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In general, we do not offer any step-by-step guide for how to reproduce the
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performance graphs shown below.
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That said, if you are keenly interested in running your own performance
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benchmarks, either in an attempt to reproduce the results shown here or to
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measure performance of different hardware, of different implementations (or
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versions), and/or for different problem sizes, you should begin by studying
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the source code, `Makefile`, and scripts in
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the [test/sup](https://github.com/flame/blis/tree/master/test/sup) directory
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of the BLIS source distribution. Then, you'll need to take time to build
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and/or install some (or all) of the implementations shown (e.g.
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[OpenBLAS](https://github.com/xianyi/OpenBLAS),
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[MKL](https://software.intel.com/en-us/mkl/),
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[Eigen](http://eigen.tuxfamily.org),
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[BLASFEO](https://github.com/giaf/blasfeo), and
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[libxsmm](https://github.com/hfp/libxsmm)), including BLIS. Be sure to consult
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the detailed notes provided below; they should be *very* helpful in successfully
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building the libraries. The `runme.sh` script in `test/sup` will help you run
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some (or all) of the test drivers produced by the `Makefile`, and the
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Matlab/Octave function `plot_panel_trxsh()` defined in the `octave` directory
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will help you turn the output of those test drivers into a PDF file of graphs.
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The `runthese.m` file will contain example invocations of the function.
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# Level-3 performance
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## Kaby Lake
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### Kaby Lake experiment details
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* Location: undisclosed
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* Processor model: Intel Core i5-7500 (Kaby Lake)
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* Core topology: one socket, 4 cores total
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* SMT status: unavailable
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* Max clock rate: 3.8GHz (single-core)
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* Max vector register length: 256 bits (AVX2)
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* Max FMA vector IPC: 2
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* Peak performance:
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* single-core: 57.6 GFLOPS (double-precision), 115.2 GFLOPS (single-precision)
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* Operating system: Gentoo Linux (Linux kernel 5.2.4)
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* Page size: 4096 bytes
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* Compiler: gcc 8.3.0
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* Results gathered: 23-28 August 2019
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* Implementations tested:
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* BLIS 4a0a6e8 (0.6.0-28)
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* configured with `./configure --enable-cblas auto`
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* sub-configuration exercised: `haswell`
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* OpenBLAS 0.3.7
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* configured `Makefile.rule` with `BINARY=64 NO_LAPACK=1 NO_LAPACKE=1 USE_THREAD=0` (single-threaded)
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* BLASFEO 01f6b7f
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* configured `Makefile.rule` with: `BLAS_API=1 FORTRAN_BLAS_API=1 CBLAS_API=1`.
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* Eigen 3.3.90
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* Obtained via the [Eigen git mirror](https://github.com/eigenteam/eigen-git-mirror) (28 August 2019)
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* Prior to compilation, modified top-level `CMakeLists.txt` to ensure that `-march=native` was added to `CXX_FLAGS` variable (h/t Sameer Agarwal):
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```
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# These lines added after line 67.
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check_cxx_compiler_flag("-march=native" COMPILER_SUPPORTS_MARCH_NATIVE)
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if(COMPILER_SUPPORTS_MARCH_NATIVE)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native")
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endif()
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```
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* configured and built BLAS library via `mkdir build; cd build; CC=gcc cmake ..; make blas`
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* installed headers via `cmake . -DCMAKE_INSTALL_PREFIX=$HOME/flame/eigen; make install`
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* The `gemm` implementation was pulled in at compile-time via Eigen headers; other operations were linked to Eigen's BLAS library.
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* Requested threading via `export OMP_NUM_THREADS=1` (single-threaded)
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* MKL 2019 update 4
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* Requested threading via `export MKL_NUM_THREADS=1` (single-threaded)
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* libxsmm 77a295c (1.6.5-6679)
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* compiled with `make AVX=2`; linked with [netlib BLAS](http://www.netlib.org/blas/) 3.6.0 as the fallback library to better show where libxsmm stops handling the computation internally.
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* Affinity:
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* N/A.
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* Frequency throttling (via `cpupower`):
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* Driver: intel_pstate
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* Governor: performance
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* Hardware limits: 800MHz - 3.8GHz
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* Adjusted minimum: 3.7GHz
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* Comments:
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* libxsmm is highly competitive for very small problems, but quickly gives up once the "large" dimension exceeds about 180-240 (or 64 in the case where all operands are square). Also, libxsmm's `gemm` cannot handle a transposition on matrix A and similarly dispatches the fallback implementation for those cases. libxsmm also does not export CBLAS interfaces, and therefore only appears on the graphs for column-stored matrices.
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### Kaby Lake results
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#### pdf
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* [Kaby Lake row-stored](graphs/sup/dgemm_rrr_kbl_nt1.pdf)
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* [Kaby Lake column-stored](graphs/sup/dgemm_ccc_kbl_nt1.pdf)
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#### png (inline)
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* **Kaby Lake row-stored**
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* **Kaby Lake column-stored**
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---
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## Haswell
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### Haswell experiment details
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* Location: TACC (Lonestar5)
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* Processor model: Intel Xeon E5-2690 v3 (Haswell)
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* Core topology: two sockets, 12 cores per socket, 24 cores total
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* SMT status: enabled, but not utilized
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* Max clock rate: 3.5GHz (single-core), 3.1GHz (multicore)
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* Max vector register length: 256 bits (AVX2)
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* Max FMA vector IPC: 2
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* Peak performance:
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* single-core: 56 GFLOPS (double-precision), 112 GFLOPS (single-precision)
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* Operating system: Cray Linux Environment 6 (Linux kernel 4.4.103)
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* Page size: 4096 bytes
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* Compiler: gcc 7.3.0
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* Results gathered: 23-28 August 2019
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* Implementations tested:
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* BLIS 4a0a6e8 (0.6.0-28)
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* configured with `./configure --enable-cblas auto`
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* sub-configuration exercised: `haswell`
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* OpenBLAS 0.3.7
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* configured `Makefile.rule` with `BINARY=64 NO_LAPACK=1 NO_LAPACKE=1 USE_THREAD=0` (single-threaded)
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* BLASFEO 01f6b7f
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* configured `Makefile.rule` with: `BLAS_API=1 FORTRAN_BLAS_API=1 CBLAS_API=1`.
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* Eigen 3.3.90
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* Obtained via the [Eigen git mirror](https://github.com/eigenteam/eigen-git-mirror) (28 August 2019)
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* Prior to compilation, modified top-level `CMakeLists.txt` to ensure that `-march=native` was added to `CXX_FLAGS` variable (h/t Sameer Agarwal):
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```
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# These lines added after line 67.
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check_cxx_compiler_flag("-march=native" COMPILER_SUPPORTS_MARCH_NATIVE)
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if(COMPILER_SUPPORTS_MARCH_NATIVE)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native")
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endif()
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```
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* configured and built BLAS library via `mkdir build; cd build; CC=gcc cmake ..; make blas`
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* installed headers via `cmake . -DCMAKE_INSTALL_PREFIX=$HOME/flame/eigen; make install`
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* The `gemm` implementation was pulled in at compile-time via Eigen headers; other operations were linked to Eigen's BLAS library.
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* Requested threading via `export OMP_NUM_THREADS=1` (single-threaded)
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* MKL 2019 update 4
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* Requested threading via `export MKL_NUM_THREADS=1` (single-threaded)
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* libxsmm 77a295c (1.6.5-6679)
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* compiled with `make AVX=2`; linked with [netlib BLAS](http://www.netlib.org/blas/) 3.6.0 as the fallback library to better show where libxsmm stops handling the computation internally.
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* Affinity:
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* N/A.
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* Frequency throttling (via `cpupower`):
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* No changes made.
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* Comments:
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* libxsmm is highly competitive for very small problems, but quickly gives up once the "large" dimension exceeds about 180-240 (or 64 in the case where all operands are square). Also, libxsmm's `gemm` cannot handle a transposition on matrix A and similarly dispatches the fallback implementation for those cases. libxsmm also does not export CBLAS interfaces, and therefore only appears on the graphs for column-stored matrices.
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### Haswell results
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#### pdf
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* [Haswell row-stored](graphs/sup/dgemm_rrr_has_nt1.pdf)
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* [Haswell column-stored](graphs/sup/dgemm_ccc_has_nt1.pdf)
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#### png (inline)
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* **Haswell row-stored**
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* **Haswell column-stored**
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---
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## Epyc
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### Epyc experiment details
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* Location: Oracle cloud
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* Processor model: AMD Epyc 7551 (Zen1)
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* Core topology: two sockets, 4 dies per socket, 2 core complexes (CCX) per die, 4 cores per CCX, 64 cores total
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* SMT status: enabled, but not utilized
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* Max clock rate: 3.0GHz (single-core), 2.55GHz (multicore)
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* Max vector register length: 256 bits (AVX2)
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* Max FMA vector IPC: 1
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* Alternatively, FMA vector IPC is 2 when vectors are limited to 128 bits each.
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* Peak performance:
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* single-core: 24 GFLOPS (double-precision), 48 GFLOPS (single-precision)
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* Operating system: Ubuntu 18.04 (Linux kernel 4.15.0)
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* Page size: 4096 bytes
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* Compiler: gcc 7.4.0
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* Results gathered: 23-28 August 2019
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* Implementations tested:
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* BLIS 4a0a6e8 (0.6.0-28)
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* configured with `./configure --enable-cblas auto`
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* sub-configuration exercised: `zen`
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* OpenBLAS 0.3.7
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* configured `Makefile.rule` with `BINARY=64 NO_LAPACK=1 NO_LAPACKE=1 USE_THREAD=0` (single-threaded)
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* BLASFEO 01f6b7f
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* configured `Makefile.rule` with: `BLAS_API=1 FORTRAN_BLAS_API=1 CBLAS_API=1`.
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* Eigen 3.3.90
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* Obtained via the [Eigen git mirror](https://github.com/eigenteam/eigen-git-mirror) (28 August 2019)
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* Prior to compilation, modified top-level `CMakeLists.txt` to ensure that `-march=native` was added to `CXX_FLAGS` variable (h/t Sameer Agarwal):
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```
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# These lines added after line 67.
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check_cxx_compiler_flag("-march=native" COMPILER_SUPPORTS_MARCH_NATIVE)
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if(COMPILER_SUPPORTS_MARCH_NATIVE)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native")
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endif()
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```
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* configured and built BLAS library via `mkdir build; cd build; CC=gcc cmake ..; make blas`
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* installed headers via `cmake . -DCMAKE_INSTALL_PREFIX=$HOME/flame/eigen; make install`
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* The `gemm` implementation was pulled in at compile-time via Eigen headers; other operations were linked to Eigen's BLAS library.
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* Requested threading via `export OMP_NUM_THREADS=1` (single-threaded)
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* MKL 2019 update 4
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* Requested threading via `export MKL_NUM_THREADS=1` (single-threaded)
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* libxsmm 77a295c (1.6.5-6679)
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* compiled with `make AVX=2`; linked with [netlib BLAS](http://www.netlib.org/blas/) 3.6.0 as the fallback library to better show where libxsmm stops handling the computation internally.
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* Affinity:
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* N/A.
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* Frequency throttling (via `cpupower`):
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* Driver: acpi-cpufreq
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* Governor: performance
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* Hardware limits: 1.2GHz - 2.0GHz
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* Adjusted minimum: 2.0GHz
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* Comments:
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* libxsmm is highly competitive for very small problems, but quickly gives up once the "large" dimension exceeds about 180-240 (or 64 in the case where all operands are square). Also, libxsmm's `gemm` cannot handle a transposition on matrix A and similarly dispatches the fallback implementation for those cases. libxsmm also does not export CBLAS interfaces, and therefore only appears on the graphs for column-stored matrices.
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### Epyc results
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#### pdf
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* [Epyc row-stored](graphs/sup/dgemm_rrr_epyc_nt1.pdf)
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* [Epyc column-stored](graphs/sup/dgemm_ccc_epyc_nt1.pdf)
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#### png (inline)
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* **Epyc row-stored**
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* **Epyc column-stored**
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---
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# Feedback
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Please let us know what you think of these performance results! Similarly, if you have any questions or concerns, or are interested in reproducing these performance experiments on your own hardware, we invite you to [open an issue](https://github.com/flame/blis/issues) and start a conversation with BLIS developers.
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Thanks for your interest in BLIS!
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