Commit c15536a0 authored by Dorothea Vom Bruch's avatar Dorothea Vom Bruch
Browse files

split readmes for different topics

parent 59dab414
......@@ -60,6 +60,8 @@ def VELO_sequence():
velo_consolidate_tracks = velo_consolidate_tracks_t()
#saxpy = saxpy_t()
velo_sequence = Sequence(
populate_odin_banks, host_global_event_cut,
......@@ -70,6 +72,8 @@ def VELO_sequence():
velo_copy_track_hit_number, prefix_sum_offsets_velo_track_hit_number,
velo_consolidate_tracks# ,
# saxpy
return velo_sequence
Generated files brief README
This folder will contain the files generated with the Allen configuration manager.
In order to generate a configuration, from the `configuration/generator/` folder follow the next steps:
* Invoke `./`. The generated file `` contains information of the algorithms in the C++ code.
* Write your own configuration. You may `import algorithms` from a python shell and check with auto-complete the algorithms available and their options. Printing any one algorithm will tell you its parameters and properties. You have also some examples in the `configuration/generator/` folder, such as ``, `` and so on.
* Invoke the `generate` method of a sequence. This will generate three files: A `ConfiguredSequence.h` containing the C++ generated code to successfully compile the code. A `Configuration.json` file, which can be used to configure the application when invoking it. Finally, a `ConfigurationGuide.json`, with all available options and their default values, for reference.
* Copy the generated `ConfiguredSequence.h` onto `configuration/sequences/`, and the `Configuration.json` onto `configuration/constants/`. Now you should be able to compile your sequence by issuing `cmake -DSEQUENCE=ConfiguredSequence .. && make`. You can invoke the program with your options with `./Allen --configuration=../configuration/constants/Configuration.json`.
Allen is configured with the python scripts located in `generator`.
In order to generate a configuration, from the `configuration/generator/` folder follow the next steps:
* Invoke `./`. The generated file `` contains information of the algorithms in the C++ code.
* Write your own configuration. You may `import algorithms` from a python shell and check with auto-complete the algorithms available and their options. Printing any one algorithm will tell you its parameters and properties. You have also some examples in the `configuration/generator/` folder, such as ``, `` and so on.
* Invoke the `generate` method of a sequence. This will generate three files: A `ConfiguredSequence.h` containing the C++ generated code to successfully compile the code. A `Configuration.json` file, which can be used to configure the application when invoking it. Finally, a `ConfigurationGuide.json`, with all available options and their default values, for reference.
* Copy the generated `ConfiguredSequence.h` onto `configuration/sequences/`, and the `Configuration.json` onto `configuration/constants/`. Now you should be able to compile your sequence by issuing `cmake -DSEQUENCE=ConfiguredSequence .. && make`. You can invoke the program with your options with `./Allen --configuration=../configuration/constants/Configuration.json`.
### Integrating the algorithm in the sequence
`Allen` centers around the idea of running a __sequence of algorithms__ on input events. This sequence is predefined and will always be executed in the same order.
Some events from the input will be discarded throughout the execution, and only a fraction of them will be kept for further processing. That is conceptually the idea behind the _High Level Trigger 1_ stage of LHCb, and is what is intended to achieve with this project.
Therefore, we need to add our algorithm to the sequence of algorithms. First, make the folder visible to CMake by editing the file `stream/CMakeLists.txt` and adding:
Then, add the following include to `stream/setup/include/ConfiguredSequence.cuh`:
#include "Saxpy.cuh"
Now, we are ready to add our algorithm to a sequence. All available sequences live in the folder `configuration/sequences/`. The sequence to execute can be chosen at compile time, by appending the name of the desired sequence to the cmake call: `cmake -DSEQUENCE=DefaultSequence ..`. For now, let's just edit the `DefaultSequence`. Add the algorithm to `configuration/sequences/DefaultSequence.h` as follows:
* Specify here the algorithms to be executed in the sequence,
* in the expected order of execution.
Keep in mind the order matters, and will define when your algorithm is scheduled. In this case, we have chosen to add it after the algorithm identified by `consolidate_tracks_t`.
Next, we need to define the arguments to be passed to our function. We need to define them in order for the dynamic scheduling machinery to properly work - that is, allocate what is needed only when it's needed, and manage the memory for us.
We will distinguish arguments just passed by value from pointers to device memory. We don't need to schedule those simply passed by value like `n` and `a`. We care however about `x` and `y`, since they require some reserving and freeing in memory.
In the algorithm definition we used the arguments `dev_x` and `dev_y`. We need to define the arguments, to make them available to our algorithm. Let's add these types to the common arguments, in `stream/setup/include/ArgumentsCommon.cuh`:
ARGUMENT(dev_x, float)
ARGUMENT(dev_y, float)
Optionally, some types are required to live throughout the whole sequence since its creation. An argument can be specified to be persistent in memory by adding it to the `output_arguments_t` tuple, in `AlgorithmDependencies.cuh`:
* @brief Output arguments, ie. that cannot be freed.
* @details The arguments specified in this type will
* be kept allocated since their first appearance
* until the end of the sequence.
typedef std::tuple<
> output_arguments_t;
### Preparing and invoking the algorithms in the sequence
Now all the pieces are in place, we are ready to prepare the algorithm and do the actual invocation.
First go to `stream/sequence/include/HostBuffers.cuh` and add the saxpy host memory pointer:
// Pinned host datatypes
uint* host_velo_tracks_atomics;
uint* host_velo_track_hit_number;
uint* host_velo_track_hits;
uint* host_total_number_of_velo_clusters;
uint* host_number_of_reconstructed_velo_tracks;
uint* host_accumulated_number_of_hits_in_velo_tracks;
uint* host_accumulated_number_of_ut_hits;
// Saxpy
int saxpy_N = 1<<20;
float *host_x, *host_y;
Reserve that host memory in `stream/sequence/src/`:
cudaCheck(cudaMallocHost((void**)&host_velo_tracks_atomics, (2 * max_number_of_events + 1) * sizeof(int)));
cudaCheck(cudaMallocHost((void**)&host_velo_track_hit_number, max_number_of_events * VeloTracking::max_tracks * sizeof(uint)));
cudaCheck(cudaMallocHost((void**)&host_velo_track_hits, max_number_of_events * VeloTracking::max_tracks * VeloTracking::max_track_size * sizeof(Velo::Hit)));
cudaCheck(cudaMallocHost((void**)&host_total_number_of_velo_clusters, sizeof(uint)));
cudaCheck(cudaMallocHost((void**)&host_number_of_reconstructed_velo_tracks, sizeof(uint)));
cudaCheck(cudaMallocHost((void**)&host_accumulated_number_of_hits_in_velo_tracks, sizeof(uint)));
cudaCheck(cudaMallocHost((void**)&host_veloUT_tracks, max_number_of_events * VeloUTTracking::max_num_tracks * sizeof(VeloUTTracking::TrackUT)));
cudaCheck(cudaMallocHost((void**)&host_atomics_veloUT, VeloUTTracking::num_atomics * max_number_of_events * sizeof(int)));
cudaCheck(cudaMallocHost((void**)&host_accumulated_number_of_ut_hits, sizeof(uint)));
cudaCheck(cudaMallocHost((void**)&host_accumulated_number_of_scifi_hits, sizeof(uint)));
// Saxpy memory allocations
cudaCheck(cudaMallocHost((void**)&host_x, saxpy_N * sizeof(float)));
cudaCheck(cudaMallocHost((void**)&host_y, saxpy_N * sizeof(float)));
Finally, create a visitor for your newly created algorithm. Create a containing folder structure for it in `stream/visitors/test/src/`, and a new file inside named ``. Insert the following code inside:
#include "SequenceVisitor.cuh"
#include "Saxpy.cuh"
void SequenceVisitor::set_arguments_size<saxpy_t>(
saxpy_t::arguments_t arguments,
const RuntimeOptions& runtime_options,
const Constants& constants,
const HostBuffers& host_buffers)
// Set arguments size
int saxpy_N = 1<<20;
void SequenceVisitor::visit<saxpy_t>(
saxpy_t& state,
const saxpy_t::arguments_t& arguments,
const RuntimeOptions& runtime_options,
const Constants& constants,
HostBuffers& host_buffers,
cudaStream_t& cuda_stream,
cudaEvent_t& cuda_generic_event)
// Saxpy test
int saxpy_N = 1<<20;
for (int i = 0; i < saxpy_N; i++) {
host_buffers.host_x[i] = 1.0f;
host_buffers.host_y[i] = 2.0f;
// Copy memory from host to device
saxpy_N * sizeof(float),
saxpy_N * sizeof(float),
// Setup opts for kernel call
state.set_opts(dim3((saxpy_N+255)/256), dim3(256), cuda_stream);
// Setup arguments for kernel call
// Kernel call
// Retrieve result
// Wait to receive the result
cudaEventRecord(cuda_generic_event, cuda_stream);
// Check the output
float maxError = 0.0f;
for (int i=0; i<saxpy_N; i++) {
maxError = std::max(maxError, abs(host_buffers.host_y[i]-4.0f));
info_cout << "Saxpy max error: " << maxError << std::endl << std::endl;
As a last step, add the visitor to `stream/CMakeLists.txt`:
file(GLOB stream_visitors_test "visitors/test/src/*cu")
add_library(Stream STATIC
We can compile the code and run the program `./Allen`. If everything went well, the following text should appear:
Saxpy max error: 0.00
The cool thing is your algorithm is now part of the sequence. You can see how memory is managed, taking into account your algorithm, and how it changes on every step by appending the `-p` option: `./Allen -p`
Sequence step 13 "saxpy_t" memory segments (MiB):
dev_velo_track_hit_number (0.01), unused (0.05), dev_atomics_storage (0.00), unused (1.30), dev_velo_track_hits (0.26), dev_x (4.00), dev_y (4.00), unused (1014.39),
Max memory required: 9.61 MiB
Adding configurable parameters
To allow a parameter to be configurable via the JSON configuration interface, a `Property` must be
added to the corresponding `ALGORITHM` call. This makes uses of variadic macros so multiple `Property`
objects can be included and will be appended verbatim to the class definition written by the `ALGORITHM` macro.
For example, the following code will add two properties to the `search_by_triplet` algorithm:
Property<float> m_tol {this,
Property<float> m_scat {this,
"scatter forwarding"};
The arguments passed to the `Property` constructor are
* the `Algorithm` that "owns" it;
* the name of the property in the JSON configuration;
* the underlying variable - this must be in `__constant__` memory for regular properties (see below);
* the default value of the property;
* a description of the property.
As the underlying parameters make use of GPU constant memory, they may not be defined within the
algorithm's class. They should instead be placed inside of namespace of the same name within the
`Configuration` namespace. For the example above, the following needs to be added to the header file:
namespace Configuration {
namespace velo_search_by_triplet_t {
// Forward tolerance in phi
extern __constant__ float forward_phi_tolerance;
// Max scatter for forming triplets (seeding) and forwarding
extern __constant__ float max_scatter_forwarding;
} // namespace velo_search_by_triplet_t
} // namespace Configuration
and the following to the code file:
__constant__ float Configuration::velo_search_by_triplet_t::forward_phi_tolerance;
__constant__ float Configuration::velo_search_by_triplet_t::max_scatter_forwarding;
Finally, the following can be added to the configuration file (default: `configuration/constants/default.json`)
to configure the values of these parameters at runtime:
"velo_search_by_triplet_t": {"forward_phi_tolerance" : "0.052", "max_scatter_forwarding" : "0.1"}
Derived properties
For properties derived from other configurable properties, the `DerivedProperty` class may be used:
Property<float> m_slope {this,
0.010f * Gaudi::Units::mrad,
"sigma velo slope [radians]"};
DerivedProperty<float> m_inv_slope {this,
std::vector<Property<float>*> {&this->m_slope},
"inv sigma velo slope"};
Here, the value of the `m_inv_slope` property is determined by the function and the
vector of properties given in the third and fourth arguments. Additional functions
may be added to the `Configuration::Relations` and defined in `stream/gear/src/`.
All functions take a vector of properties as an argument, to allow for functions of an
arbitrary number of properties.
CPU properties
Regular properties are designed to be used in GPU algorithms and are stored
in GPU constant memory with a cached copy within the `Property` class.
For properties that are only needed on the CPU, e.g. grid and block dimensions,
a `CPUProperty` can be used, which only stores the configured value internally.
This is also useful for properties tht are only needed when first configuring the
algorithm, such as properties only used in the visitor class.
Note that regular properties may also be used in this case
(e.g. `../stream/visitors/velo/src/` accesses non-CPU properties)
but if a property is *only* needed on the CPU then there is a reduced overhead in using a `CPUProperty`.
These are defined in the same way as a `Property` but take one fewer argument as there is no underlying
constant memory object to reference.
CPUProperty<std::array<int, 3>> m_block_dim {this, "block_dim", {32, 1, 1}, "block dimensions"};
CPUProperty<std::array<int, 3>> m_grid_dim {this, "grid_dim", {1, 1, 1}, "grid dimensions"};
Shared properties
For properties that are shared between multiple top-level algorithms, it may be preferred
to keep the properties in a neutral location. This ensures that properties are configured
regardless of which algorithms are used in the configured sequence and can be achieved by
using a `SharedProperty`.
Shared properties are owned by a `SharedPropertySet` rather than an `Algorithm`
and example of which is given below.
#include "Configuration.cuh"
namespace Configuration {
namespace example_common {
extern __constant__ float param;
struct ExampleConfiguration : public SharedPropertySet {
ExampleConfiguration() = default;
constexpr static auto name{ "example_common" };
Property<float> m_par{this, "param", Configuration::example_common::param, 0., "an example parameter"};
This may be used by any algorithm by including the header and adding the following line
to the end of the arguments of the `ALGORITHM` call.
SharedProperty<float> m_shared{this, "example_common", "param"};
These must also be plumbed in to `Configuration::getSharedPropertySet` in `stream/gear/src/`
to allow the property set to be found by algorithms.
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This diff is collapsed.
......@@ -42,7 +42,7 @@ __global__ void saxpy(Parameters);
cudaStream_t& cuda_stream,
cudaEvent_t&) const
function(dim3(value<host_number_of_selected_events_t>(arguments) / property<block_dim_t>()), property<block_dim_t>(), cuda_stream)(
function(dim3(value<host_number_of_selected_events_t>(arguments) / 32), property<block_dim_t>(), cuda_stream)(
Parameters {begin<dev_offsets_all_velo_tracks_t>(arguments),
begin<dev_saxpy_output_t>(arguments), property<saxpy_scale_factor_t>()});
Adding selection lines
This will cover how to add trigger lines to Allen that select events
based on reconstructed trigger candidates. Special lines (e.g. NoBias
or pass-through lines) should be handled on a case-by-case basis.
Writing the selection
Trigger selections should be `__device__` functions that take either a
`const ParKalmanFilter::FittedTrack&` or a `const
VertexFit::TrackMVAVertex&` as an argument and return a `bool`. For
example, a line selecting high-pT tracks might look like:
__device__ bool HighPtTrack(const ParKalmanFilter::FittedTrack& track)
return > 10.0 / Gaudi::Units::GeV
The header file for the selection should be placed in
`cuda/selections/Hlt1/include` and the implementation should be placed
in `cuda/selections/Hlt1/src`.
Adding the line to the Allen sequence
Bookkeeping information for the Hlt1 lines is found in
`cuda/selections/Hlt1/include/LineInfo.cuh`. In order for a line
to run, it must be added to `Hlt1::Hlt1Lines` and a name must be added
to `Hlt1::Hlt1LineNames`. This will ensure that space is allocated to
store the selection decision for each candidate.
Special lines are listed first, followed by 1-track lines, then 2-,
3-, and finally 4-track lines. The new line should be added to the
appropriate place in the list. In addition, the number of lines of
that type should be incremented by 1. For example, the above
`HighPtTrack` line should be added after `// Begin 1-track lines.` and
before `Begin 2-track lines.` The line name should be added at the
same position in `Hlt1::Hlt1LineNames`.
Finally, add the selection function to the relevant array of pointers
to selections (e.g. `Hlt1::OneTrackSelections` or
`Hlt1::TwoTrackSelections`). These must be in the same order as in
`Hlt1::Hlt1LineNames` and `Hlt1::Hlt1Lines`.
Adding monitoring histograms
Monitoring in Allen is performed by dedicated monitoring threads (by default there is a single thread).
After a slice of data is processed, the `HostBuffers` corresponding to that slice are sent to the monitoring
thread concurrent with being sent to the I/O thread for output. The flow of `HostBuffers` is shown below:
graph LR
A((HostBuffer<br>Manager))-->B[GPU thread]
B-->C[I/O thread]
B-->|if free|D[Monitoring thread]
To avoid excessive load on the CPU, monitoring threads will not queue `HostBuffers`, i.e, if the
monitoring thread is already busy then new `HostBuffers` will be immediately marked as monitored.
Functionality exists within `MonitorManager` to reactively reduce the amount of monitoring performed
(n.b. this corresponds to an **increase** in the `monitoring_level`) in response to a large number of skipped
slices. This is not currently used but would allow monitoring to favour running *some* types of monitors
for *all* slices over running *all* types of monitors for *some* slices. Additionally, less important monitors
could be run on a random sub-sample of slices. The `MetaMonitor` provides monitoring histograms that track
the numbers of successfully monitored and skipped slices as well as the monitoring level.
Monitor classes
Currently, monitoring is performed of the rate for each HLT line (`RateMonitor`) and for the momentum,
pT and chi^2(IP) of each track produced by the Kalman filter (`TrackMonitor`). Further monitoring histograms
can be either added to one of these classes or to a new monitoring class, as appropriate.
Additional monitors that produce histograms based on information in the `HostBuffers` should be added to
`integration/monitoring` and inherit from the `BufferMonitor` class. The `RateMonitor` class provides an
example of this. Furthermore, each histogram that is added must be given a unique key in MonitorBase::MonHistType.
Once a new monitoring class has been written, this may be added to the monitoring thread(s) by including an instance
of the class in the vectors created in `MonitorManager::init`, e.g.
m_monitors.back().push_back(new RateMonitor(buffers_manager, time_step, offset));
To monitor a feature, either that feature or others from which it can be calculated must be present in the
`HostBuffers`. For example, the features recorded by `TrackMonitor` depend on the buffers `host_kf_tracks`
(for the track objects) and `host_atomics_scifi` (for the number of tracks in each event and the offset to the
start of each event). It is important that any buffers used by the monitoring are copied from the device to
the host memory and that they do not depend on `runtime_options.do_check` being set. Additionally, to avoid
a loss of performance, these buffers must be written to pinned memory, i.e. the memory must be allocated by
`cudaMallocHost` and not by `malloc` in `HostBuffers::reserve`.
Saving histograms
All histograms may be saved by calling `MonitorManager::saveHistograms`. This is currently performed once after
Allen has finished executing. In principle, this could be performed on a regular basis within the main loop but
ideally would require monitoring threads to be paused for thread safety.
Histograms are currently written to `monitoringHists.root`.
\ No newline at end of file
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