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Copyright 2021 Huawei Technologies Co., Ltd.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
This distribution contains the C++ Algebraic Programming (ALP) framework, and provides the ALP/GraphBLAS, ALP/Pregel, and Sparse BLAS programming interfaces. Only a subset of Sparse BLAS functionality is supported, at present.
This distribution contains ALP backends that generate:
Additional backends may optionally be enabled by providing their dependences. Those backends generate:
All backends perform automatically generate vectorised programs, amongst other automatically-applied optimisations.
The ALP/GraphBLAS and ALP/Pregel interfaces are enabled for all backends, while the standard Sparse BLAS APIs only allow for the efficient support of the sequential and shared-memory parallel backends.
We first summarise the compile-time, link-time, and run-time dependences of ALP.
The following are required for producing both sequential and shared-memory ALP
libraries and programs, using its reference and reference_omp backends.
To compile ALP, you need the following tools:
The ALP libraries link against the following libraries:
-lnuma
-lm
-lpthread
-fopenmp in the case of GCCThe below summarises the dependences for optional features.
For distributed-memory parallelisation, the Lightweight Parallel Foundations (LPF) communication layer, version 1.0 or higher, is required. ALP makes use of the LPF core library and its collectives library. The LPF library has its further dependences, which are all summarised on the LPF project page:
The dependence on LPF applies to compilation, linking, and run-time. Fulfilling
the dependence enables the bsp1d and hybrid ALP/GraphBLAS backends.
For generating the code documentations:
doxygen reads code comments and generates the documentation;graphviz generates various diagrams for inheritance, call paths, etc.;pdflatex is required to build the PDF file out of the Latex generated
documentation.Here are example steps to compile and install ALP for shared-memory machines
without distributed-memory support. The last three commands show-case the
compilation and execution of the sp.cpp example program.
cd <ALP/root/dir>
mkdir build
cd build
../bootstrap.sh --prefix=../install
make -j
make -j install
source ../install/bin/setenv
grbcxx ../examples/sp.cpp
grbrun ./a.out
In more detail, the steps to follow are:
Edit the include/graphblas/base/config.hpp. In particular, please ensure
that config::SIMD_SIZE::bytes defined in that file is set correctly with
respect to the target architecture.
Create an empty directory for building ALP and move into it:
mkdir build && cd build.
Invoke the bootstrap.sh script located inside the ALP root directory
<ALP/root/dir> to generate the build infrastructure via CMake inside the
the current directory:
<ALP/root/dir>/bootstrap.sh --prefix=</path/to/install/dir>
--with-lpf=/path/to/lpf/install/dir if you have LPF installed
and would like to use it.Issue make -j to compile the C++11 ALP library for the configured backends.
(Optional) To later run all unit tests, several datasets must be made
available. Please run the <ALP/root/dir>/tools/downloadDatasets.sh
script for
a. an overview of datasets required for the basic tests, as well as
b. the option to automatically download them.
(Optional) To make the ALP documentation, issue make userdocs. This
generates both
a. LaTeX in <ALP build dir>/docs/user/latex/refman.tex, and
b. HTML in <ALP build dir>/docs/user/html/index.html.
To build a PDF from the LaTeX sources, cd into the directory mentioned, and
issue make.
(Optional) Issue make -j smoketests to run a quick set of functional
tests. Please scan the output for any failed tests.
If you do this with LPF enabled, and LPF was configured to use an MPI engine
(which is the default), and the MPI implementation used is not MPICH, then
the default command lines the tests script uses are likely wrong. In this
case, please edit tests/parse_env.sh by searching for the MPI
implementation you used, and uncomment the lines directly below each
occurrence.
(Optional) Issue make -j unittests to run an exhaustive set of unit
tests. Please scan the output for any failed tests.
If you do this with LPF enabled, please edit tests/parse_env.sh if required
as described in step 5.
Issue make -j install to install ALP into the install directory configured
during step 1.
(Optional) Issue source </path/to/install/dir>/bin/setenv to make
available the grbcxx and grbrun compiler wrapper and runner.
Congratulations, you are now ready for developing and integrating ALP algorithms! Any feedback, question, problem reports are most welcome at
The remainder of this file summarises configuration options, additional build system targets, how to integrate ALP programs into applications, debugging, and contribute to ALP development. Finally, this README acknowledges contributors and lists technical papers.
ALP employs configuration headers that contain constexpr settings that take
effect every time ALP programs are compiled. Multiple object files that were
compiled using ALP must all been compiled using the same configuration
settings-- linking objects that have been compiled with a mixture of
configurations are likely to incur undefined behaviour. The recommendation is
to set a configuration before building and installing ALP, and to keep the
installation directories read-only so that configurations remain static.
There exists one main configuration file that affects all ALP backends, while
other configuration files only affect a specific backend or only affect specific
classes of backends. The main configuration file is found in
<root>/include/graphblas/base/config.hpp, which allows one to set the
CACHE_LINE_SIZE class;SIMD_SIZE class;BENCHMARKING class;MEMORY::big_memory class;MEMORY::big_memory;RowIndexType typedef;ColIndexType typedef;NonzeroIndexType typedef;VectorIndexType typedef.Other configuration values in this file are automatically inferred, are fixed non-configurable settings, or are presently not used by any ALP backend.
The file include/graphblas/reference/config.hpp contain defaults that pertain
to the auto-vectorising and sequential reference backend, but also to the
shared-memory auto-parallelising reference_omp backend. It allows one to set
PREFETCHING::enabled;PREFETCHING::distance;IMPLEMENTATION::defaultAllocMode();IMPLEMENTATION::sharedAllocMode();Modifying any of the above should be done with utmost care as it typically affects the defaults across an ALP installation, and all programs compiled using it. Configuration elements not mentioned here should not be touched by users, and rather should concern ALP developers only.
The file include/graphblas/omp/config.hpp contains some basic configuration
parameters that affect any OpenMP-based backend. However, the configuration
file does not contain any other user-modifiable settings, but rather contains
a) some utilities that OpenMP-based backends may rely on, and b) default
that are derived from other settings described in the above. These settings
should only be overridden with compelling and expert knowledge.
The file include/graphblas/bsp/config.hpp contains some basic configuration
parameters that affect any LPF-based backend. It includes:
LPF::regs();LPF::maxh().These defaults, if insufficient, will be automatically resized during execution. Setting these large enough will therefore chiefly prevent buffer resizes at run- time. Modifying these should normally not lead to significant performance differences.
The file include/graphblas/utils/config.hpp details configurations of various
utility functions, including:
PARSER::bsize();PARSER::read_bsize().These defaults are usually fine except when reading from SSDs, which would
benefit of a larger read_bsize.
While there are various other configuration files (find config.hpp), the above
should list all user-modifiable configuration settings of interest. The
remainder pertain to configurations that are automatically deduced from the
aforementioned settings, or pertain to settings that describe how to safely
compose backends and thus only are of interest to ALP developers.
The following table lists the main build targets of interest:
| Target | Explanation |
|---|---|
| [default] | builds the ALP libraries and examples, including |
| Sparse BLAS libraries generated by ALP | |
install |
install libraries, headers and some convenience |
scripts into the path set via --prefix=<path>
|
|
unittests |
builds and runs all available unit tests |
smoketests |
builds and runs all available smoke tests |
perftests |
builds and runs all available performance tests |
tests |
builds and runs all available unit, smoke, and |
| performance tests | |
userdocs |
builds HTML and LaTeX documentation corresponding |
| to the public ALP API | |
devdocs |
builds HTML and LaTeX code documentation for |
| developers of the ALP internals | |
docs |
build both the user and developer code |
| documentation |
For more information about the testing harness, please refer to the related documentation.
For more information on how the build and test infrastructure operate, please refer to the the related documentation.
To check in-depth performance of this ALP implementation, issue
make -j perftests. This will run several algorithms in several ALP
configurations. This generates three main output files:
<ALP/build/dir>/tests/performance/output, which summarises the whole run;
<ALP/build/dir>/tests/performance/output/benchmarks, which summarises the
performance of individual algorithms; and
<ALP/build/dir>/tests/performance/output/scaling, which summarises operator
scaling results.
To ensure that all tests run, please ensure that all related datasets are available, as also described at step 5 of the quick start.
With LPF enabled, please note the remark described at steps 3 and 7 of the quick
start guide. If LPF was not configured using MPICH, please review and apply any
necessary changes to tests/performance/performancetests.sh.
There are several use cases in which ALP can be deployed and utilised, listed
in the following. These assume that the user has installed ALP in a dedicated
directory via make install.
The grb::Launcher< AUTOMATIC > class abstracts a group of user processes that
should collaboratively execute any single ALP program. The ALP program of
interest must have the following signature:
void grb_program( const T& input_data, U& output_data )
The types T and U can be any plain-old-data (POD) type, including structs --
these can be used to broadcast input data from the master process to all user
processes (input_data) -- and for data to be sent back on exit of the parallel
ALP program.
The above sending-and-receiving across processes applies only to ALP
implementations and backends that support or require multiple user processes;
both the sequential reference and the shared-memory parallel reference_omp
backends, for example, support only one user process.
In case of multiple user processes, the overhead of the broadcasting of input
data is linear in the number of user processes, as well as linear in the byte-
size of T which hence should be kept to a minimum. A recommended use of this
mechanism is, e.g., to broadcast input data locations; any additional I/O
should use the parallel I/O mechanisms that ALP exposes to the ALP program
itself.
Output data is retrieved only from the user process with ID 0, even if
multiple user processes exist. Some implementations or systems may require
sending back the output data to a calling process, even if there is only
one user process. The data movement cost incurred should hence be considered
linear in the byte size of U, and, similar to the input data broadcasting,
the use of parallel I/O facilities from the ALP program itself for storing
large outputs is strongly advisable.
Our backends auto-vectorise, hence please recall step 1 from the quick start
guide, and make sure the include/graphblas/base/config.hpp file reflects the
correct value for config::SIMD_SIZE::bytes. This value must be updated prior
to the compilation and installation of ALP.
When targeting different architectures with differing SIMD widths, different ALP installations for different architectures could be maintained.
ALP programs may be compiled using the compiler wrapper grbcxx that is
generated during installation. To compile high-performance code when compiling
your programs using the ALP installation, the following flags are recommended:
-DNDEBUG -O3 -mtune=native -march=native -funroll-loopsOmitting these flags for brevity, some compilation examples follow.
When using the LPF-enabled hybrid shared- and distributed-memory ALP backends,
grbcxx -b hybrid
as the compiler command. To show all flags that the wrapper passes on, please use
grbcxx -b hybrid --show
and append your regular compilation arguments.
The hybrid backend is capable of spawning multiple ALP user processes. In
contrast, compilation using
grbcxx -b reference
produces a sequential binary, while
grbcxx -b reference_omp
produces a shared-memory parallel binary.
Note that the ALP source code never requires change while switching backends.
The executable must be statically linked against an ALP library that is
different depending on the selected backend.
The compiler wrapper grbcxx takes care of all link-time dependencies
automatically.
When using the LPF-enabled BSP1D backend to ALP, for example, simply use
grbcxx -b bsp1d as the compiler/linker command.
Use
grbcxx -b bsp1d --show <your regular compilation command>
to show all flags that the wrapper passes on.
The resulting program has run-time dependencies that are taken care of by the
LPF runner lpfrun or by the ALP runner grbrun.
We recommend using the latter:
grbrun -b hybrid -np <P> </path/to/my/program>
Here, P is the number of requested ALP user processes.
The hybrid backend employs threading in addition to distributed-memory
parallelism. To employ threading to use all available hyper-threads or cores
on a single node, the reference_omp backend may be selected instead.
In both cases, make sure that during execution the OMP_NUM_THREADS and
OMP_PROC_BIND environment variables are set appropriately on each node that
executes ALP user process(es).
This, instead of automatically spawning a requested number of user processes, assumes a number of processes already exist and that we wish those processes to jointly execute a single parallel ALP program.
The binary that contains the ALP program to be executed must define the following global symbol with the given value:
const int LPF_MPI_AUTO_INITIALIZE = 0
A program may then again be launched via the Launcher, but in this case the
MANUAL template argument should be used instead.
This specialisation disallows the use of a default constructor.
Instead, construction requires four arguments as follows:
grb::Launcher< MANUAL > launcher( s, P, hostname, portname )
Here, P is the total number of processes that should jointly execute a
parallel ALP program, while 0 <= s < P is a unique ID of this process amongst
its P-1 siblings. The types of s and P are size_t, i.e., unsigned
integers.
One of these processes must be selected as a connection broker prior to forming
a group of ALP user processes. The remainder P-1 processes must first connect
to the chosen broker using TCP/IP connections. This choice must be made outside
of ALP, prior to setting up the launcher, and materialises as the hostname and
portname Launcher constructor arguments. The host and port name are strings,
and must be equal across all processes.
As before, and after the successful construction of a manual launcher instance, a parallel ALP program is launched via
grb::Launcher< MANUAL >::exec( &grb_program, input, output )
in exactly the same way as described earlier, though with the input and output arguments now being passed in a one-to-one fashion:
Launcher< MANUAL >::exec. To share ALP vector data,
it is, for example, legal to return a grb::PinnedVector< T > as the
exec output argument type. Doing so is akin to returning a pointer to
output data, and does not explicitly pack nor transmit vector data.The pre-existing process must have been started using an external mechanism. This mechanism must include run-time dependence information that is normally passed by the ALP runner whenever a distributed-memory parallel backend is selected.
If the external mechanism by which the original processes are started allows it,
this is most easily effected by using the standard grbcxx launcher while
requesting only one process only, e.g.,
grbrun -b hybrid -n 1 </your/executable>
If the external mechanism does not allow this, then please execute e.g.
grbrun -b hybrid -n 1 --show </any/executable>
to inspect the run-time dependences and environment variables that must be made available, resp., set, as part of the external mechanism that spawns the original processes.
Please see this article on how to add ALP and ALP/GraphBLAS as a dependence to your project.
To debug an ALP program, please compile it using the sequential reference
backend and use standard debugging tools such as valgrind and gdb.
Additionally, please ensure to not pass the -DNDEBUG flag during
compilation.
If bugs appear in one backend but not another, it is likely you have found a bug in the former backend. Please send a minimum working example that demonstrates the bug to the maintainers, either as an issue on or an email to:
Your contributions to ALP would be most welcome. Merge Requests (MRs) can be contributed via Gitee and GitHub; see above for the links.
For the complete development documentation, you should start from the docs/README file and the related Development guide.
The LPF communications layer was primarily authored by Wijnand Suijlen, without whom the current ALP would not be what it is now.
The collectives library and its interface to the ALP was primarily authored by Jonathan M. Nash.
The testing infrastructure that performs smoke, unit, and performance testing of sequential, shared-memory parallel, and distributed-memory parallel backends was primarily developed by Daniel Di Nardo.
ALP and ALP/GraphBLAS have since developed significantly, primarily through efforts by researchers at the Huawei Paris and Zürich Research Centres, and the Computing Systems Laboratory in Zürich in particular. See the NOTICE file for individual contributors.
If you use ALP in your work, please consider citing one or more of the following papers, as appropriate.
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