makortel / pixeltrack-standalone

Standalone Patatrack pixel tracking

Standalone Patatrack pixel tracking

Table of contents

• Introduction
• Status
• Quick recipe
  • Additional make targets
• Code structure
• Build system
• Contribution guide

Introduction

The purpose of this package is to explore various performance portability solutions with the Patatrack pixel tracking application. The version here corresponds to CMSSW_11_1_0_pre4_Patatrack.

The application is designed to require minimal dependencies on the system:

• GNU Make, curl, md5sum, tar
• C++17 capable compiler (tested with GCC 8)
• CUDA runtime and drivers (tested with CUDA 10.2)

All other external dependencies (listed below) are downloaded and built automatically.

• TBB
• CUB
• Eigen

The input data set consists of a minimal binary dump of 1000 events of ttbar+PU events from of /TTToHadronic_TuneCP5_13TeV-powheg-pythia8/RunIIAutumn18DR-PUAvg50IdealConditions_IdealConditions_102X_upgrade2018_design_v9_ext1-v2/FEVTDEBUGHLT dataset from the CMS Open Data. The data are downloaded automatically during the build process.

Status

Application	Description	Framework	Device framework	Raw2Cluster	RecHit	Pixel tracking	Vertex	Transfers to CPU
fwtest	Framework test	✔️
cudatest	CUDA FW test	✔️	✔️
cuda	CUDA version	✔️	✔️	✔️	✔️	✔️	✔️	✔️

Quick recipe

# Build application with N-fold concurrency
$ make -j N cuda

# For CUDA installations elsewhere than /usr/local/cuda
$ make -j N cuda CUDA_BASE=/path/to/cuda

# Source environment (not really necessary now, but will be needed later)
$ source env.sh

# Process 1000 events in 1 thread
$ ./cuda

# Command line arguments
$ ./cuda -h
./cuda: [--numberOfThreads NT] [--numberOfStreams NS] [--maxEvents ME] [--data PATH] [--transfer] [--validation] [--empty]

Options
 --numberOfThreads   Number of threads to use (default 1)
 --numberOfStreams   Number of concurrent events (default 0=numberOfThreads)
 --maxEvents         Number of events to process (default -1 for all events in the input file)
 --data              Path to the 'data' directory (default 'data' in the directory of the executable)
 --transfer          Transfer results from GPU to CPU (default is to leave them on GPU)
 --validation        Run (rudimentary) validation at the end (implies --transfer)
 --empty             Ignore all producers (for testing only)

Additional make targets

Target	Description
all (default)	Build all programs
clean	Remove all build artifacts
distclean	clean and remove all externals
dataclean	Remove downloaded data files
format	Format the code with clang-format

Code structure

The project is split into several programs, one (or more) for each test case. Each test case has its own directory under src directory. A test case contains the full application: framework, data formats, device tooling, plugins for the algorithmic modules ran by the framework, and the executable.

Each test program is structured as follows within src/<program name> (examples point to cuda

• Makefile that defines the actual build rules for the program
• Makefile.deps that declares the external dependencies of the program, and the dependencies between shared objects within the program
• plugins.txt contains a simple mapping from module names to the plugin shared object names
  • In CMSSW such information is generated automatically by scram, in this project the original author was lazy to automate that
• bin/ directory that contains all the framework code for the executable binary. These files should not need to be modified, except main.cc for changin the set of modules to run, and possibly more command line options
• plugin-<PluginName>/ directories contain the source code for plugins. The <PluginName> part specifies the name of the plugin, and the resulting shared object file is plugin<PluginName>.so. Note that no other library or plugin may depend on a plugin (either at link time or even thourgh #including a header). The plugins may only be loaded through the names of the modules by the PluginManager.
• <LibraryName>/: the remaining directories are for libraries. The <LibraryName> specifies the name of the library, and the resulting shared object file is lib<LibraryName>.so. Other libraries or plugins may depend on a library, in which case the dependence must be declared in Makefile.deps.
  • CondFormats/:
  • CUDADataFormats/: CUDA-specific data structures that can be passed from one module to another via the edm::Event. A given portability technology likely needs its own data format directory, the CUDADataFormats can be used as an example.
  • CUDACore/: Various tools for CUDA. A given portability technology likely needs its own tool directory, the CUDACore can be used as an example.
  • DataFormats/: mainly CPU-side data structures that can be passed from one module to another via the edm::Event. Some of these are produced by the edm::Source by reading the binary dumps. These files should not need to be modified. New classes may be added, but they should be independent of the portability technology.
  • Framework/: crude approximation of the CMSSW framework. Utilizes TBB tasks to orchestrate the processing of the events by the modules. These files should not need to be modified.
  • Geometry/: geometry information, essentially handful of compile-time constants. May be modified.

For more detailed description of the application structure (mostly plugins) see CodeStructure.md

Build system

The build system is based on pure GNU Make. There are two levels of Makefiles. The top-level Makefile handles the building of the entire project: it defines general build flags, paths to external dependencies in the system, recipes to download and build the externals, and targets for the test programs.

For more information see BuildSystem.md.

Contribution guide

Given that the approach of this project is to maintain many programs in a single branch, in order to keep the commit history readable, each commit should contain changes only for one test program, and the short commit message should start with the program name, e.g. [cuda]. A pull request may touch many test programs.

When starting work for a new portability technology, the first steps are to figure out the installation of the necessary external software packages and the build rules (both can be adjusted later). It is probably best to start by cloning the fwtest code for the new program (e.g. footest for a technology foo), adjust the test modules to exercise the API of the technology (see cudatest for examples), and start crafting the tools package (CUDACore in cuda).