diff --git a/Profiling.html b/Profiling.html new file mode 100644 index 000000000..7fcd91bc0 --- /dev/null +++ b/Profiling.html @@ -0,0 +1,206 @@ + + + + + + + +IPPL (Independent Parallel Particle Layer): Profiling in IPPL + + + + + + + + + + + +
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Profiling in IPPL
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+

In certain applications, you might want to use profiling tools for debugging and testing. Since IPPL uses Kokkos as a backend, you can leverage Kokkos' built-in profiling tools.

+

This guide explains how to use Kokkos' profiling tools, using the MemoryEvents tool as an example.

+

+Description of MemoryEvents

+

MemoryEvents tracks a timeline of allocation and deallocation events in Kokkos Memory Spaces. It records time, pointer, size, memory-space-name, and allocation-name. This is in particular useful for debugging purposes to understand where all the memory is going.

+

Additionally, the tool provides a timeline of memory usage for each individual Kokkos Memory Space.

+

The tool is located at: https://github.com/kokkos/kokkos-tools/tree/develop/profiling/memory-events

+
+

+Steps to Use Kokkos Profiling Tools

+

+1. Clone the Kokkos Tools Repository

+

First, clone the Kokkos tools repository, which contains a variety of profiling tools:

git clone https://github.com/kokkos/kokkos-tools
+

+2. Build and Install the Tools

+

Navigate into the repository and build the tools using CMake:

+
cd kokkos-tools
+
cmake ..
+
make -j
+
sudo make install
+

+3. Set Up the Profiling Tool

+

Before running your application, export the Kokkos Tools environment variable to point to the kp_memory_events.so tool:

export KOKKOS_TOOLS_LIBS={PATH_TO_TOOL_DIRECTORY}/kp_memory_events.so
+

Replace {PATH_TO_TOOL_DIRECTORY} with the actual path where the tool is located.

+

+4. Run your Application

+

Execute your application normally. The MemoryEvents tool will automatically collect data during execution. For example:

+
./application COMMANDS
+

+5. Output Files

+

The MemoryEvents tool will generate the following files:

+
    +
  • HOSTNAME-PROCESSID.mem_events: Lists memory events.
    • +
  • HOSTNAME-PROCESSID-MEMSPACE.memspace_usage: Provides a utilization timeline for each active memory space.
    • +
+

+6. Example on with SLURM

+

Here’s an example of how to run the profiling with a SLURM system using sbatch:

sbatch -n 2 --wrap="export KOKKOS_TOOLS_LIBS=$HOME/kokkos-tools/kp_memory_events.so; \
+
mpirun -n 2 LandauDamping 128 128 128 10000 10 FFT 0.01 LeapFrog --overallocate 2.0 --info 10"
+

In this example:

+
    +
  • sbatch -n 2 specifies 2 nodes.
    • +
  • The Kokkos tool is exported and applied to the LandauDamping application.
    • +
+

This guide provides the basic steps for integrating Kokkos profiling tools into your IPPL-based projects. You can adjust the commands as needed depending on your specific application and environment.

+

+Example

+

Consider the following code:

+
#include <Kokkos_Core.hpp>
+
+
typedef Kokkos::View<int*,Kokkos::CudaSpace> a_type;
+
typedef Kokkos::View<int*,Kokkos::CudaUVMSpace> b_type;
+
typedef Kokkos::View<int*,Kokkos::CudaHostPinnedSpace> c_type;
+
+
int main() {
+
Kokkos::initialize();
+
{
+
int N = 10000000;
+
for(int i =0; i<2; i++) {
+
a_type a("A",N);
+
{
+
b_type b("B",N);
+
c_type c("C",N);
+
for(int j =0; j<N; j++) {
+
b(j)=2*j;
+
c(j)=3*j;
+
}
+
}
+
}
+
}
+
Kokkos::finalize();
+
+
}
+

This will produce the following output:

+

HOSTNAME-PROCESSID.mem_events

+
# Memory Events
+
# Time Ptr Size MemSpace Op Name
+
0.311749 0x2048a0080 128 CudaHostPinned Allocate InternalScratchUnified
+
0.311913 0x2305ca0080 2048 Cuda Allocate InternalScratchFlags
+
0.312108 0x2305da0080 16384 Cuda Allocate InternalScratchSpace
+
0.312667 0x23060a0080 40000000 Cuda Allocate A
+
0.317260 0x23086e0080 40000000 CudaUVM Allocate B
+
0.335289 0x2049a0080 40000000 CudaHostPinned Allocate C
+
0.368485 0x2049a0080 -40000000 CudaHostPinned DeAllocate C
+
0.377285 0x23086e0080 -40000000 CudaUVM DeAllocate B
+
0.379795 0x23060a0080 -40000000 Cuda DeAllocate A
+
0.380185 0x23060a0080 40000000 Cuda Allocate A
+
0.384785 0x23086e0080 40000000 CudaUVM Allocate B
+
0.400073 0x2049a0080 40000000 CudaHostPinned Allocate C
+
0.433218 0x2049a0080 -40000000 CudaHostPinned DeAllocate C
+
0.441988 0x23086e0080 -40000000 CudaUVM DeAllocate B
+
0.444391 0x23060a0080 -40000000 Cuda DeAllocate A
+

HOSTNAME-PROCESSID-Cuda.memspace_usage

+
# Space Cuda
+
# Time(s) Size(MB) HighWater(MB) HighWater-Process(MB)
+
0.311913 0.0 0.0 81.8
+
0.312108 0.0 0.0 81.8
+
0.312667 38.2 38.2 81.8
+
0.379795 0.0 38.2 158.1
+
0.380185 38.2 38.2 158.1
+
0.444391 0.0 38.2 158.1
+

HOSTNAME-PROCESSID-CudaUVM.memspace_usage

+
# Space CudaUVM
+
# Time(s) Size(MB) HighWater(MB) HighWater-Process(MB)
+
0.317260 38.1 38.1 81.8
+
0.377285 0.0 38.1 158.1
+
0.384785 38.1 38.1 158.1
+
0.441988 0.0 38.1 158.1
+

HOSTNAME-PROCESSID-CudaHostPinned.memspace_usage

+
# Space CudaHostPinned
+
# Time(s) Size(MB) HighWater(MB) HighWater-Process(MB)
+
0.311749 0.0 0.0 81.8
+
0.335289 38.1 38.1 120.0
+
0.368485 0.0 38.1 158.1
+
0.400073 38.1 38.1 158.1
+
0.433218 0.0 38.1 158.1
+
+
+ +
+Generated by doxygen 1.9.5 +
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+ + diff --git a/menudata.js b/menudata.js index 84a7ccaae..641ff4aa9 100644 --- a/menudata.js +++ b/menudata.js @@ -26,6 +26,7 @@ var menudata={children:[ {text:"Main Page",url:"index.html"}, {text:"Basics Usage",url:"Basics.html"}, {text:"Installation",url:"Installation.html"}, +{text:"Profiling",url:"Profiling.html"}, {text:"Namespaces",url:"namespaces.html",children:[ {text:"Namespace List",url:"namespaces.html"}, {text:"Namespace Members",url:"namespacemembers.html",children:[ diff --git a/search/all_f.js b/search/all_f.js index 4bbf60779..cb2fe94f0 100644 --- a/search/all_f.js +++ b/search/all_f.js @@ -16,14 +16,14 @@ var searchData= ['parallel_5ffor_13',['parallel_for',['../namespaceippl.html#ab5d78bcf87f750df8cdb9546221d0f71',1,'ippl']]], ['parallel_5freduce_14',['parallel_reduce',['../namespaceippl.html#ab8faeca32cc3523c0e39f7c0e6ec7cbc',1,'ippl']]], ['paralleldispatch_2eh_15',['ParallelDispatch.h',['../ParallelDispatch_8h.html',1,'']]], - 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