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Integrating TAMPI and ParaStationMPI NAM windows

Table of contents:

Quick Overview

Heat Benchmark

In this section, we exemplify the use of TAMPI and NAM windows through the Heat benchmark. We use an iterative Gauss-Seidel method to solve the Heat equation, which is a parabolic partial differential equation that describes the distribution of heat in a given region over time. This benchmark simulates the heat diffusion on a 2D matrix of floating-point elements during multiple timesteps. The 2D matrix is logically divided into 2D blocks and may have multiple rows and columns of blocks. The computation of an element at position M[r][c] in the timestep t depends on the value of the top and left elements (M[r-1][c] and M[r][c-1]) computed in the current timestep t, and the right and bottom elements (M[r][c+1] and M[r+1][c]) from the previous timestep t-1. We can extrapolate this logic in the context of blocks so that a block has a dependency on the computation of its adjacent blocks. Notice that the computation of blocks in a diagonal is fully concurrent because there is no dependency between them.

There are three different MPI versions, and all of them distribute the 2D matrix across ranks assigning consecutive rows of blocks to each MPI rank. Note that the matrix is distributed by blocks vertically but not horizontally. Therefore, an MPI rank has two neighboring ranks: one above and another below. The exceptions are the first and last ranks since they have a single neighbor. This distribution requires the neighboring ranks to exchange the external rows (halos) from their boundary blocks in order to compute their local blocks in each timestep.

This benchmark is publicly available in the ?https://pm.bsc.es/gitlab/DEEP-EST/apps/Heat repository. The first version is based on an MPI-only parallelization, while the other two are hybrid MPI+OmpSs-2 leveraging tasks and the TAMPI library. We briefly describe each one below:

Using NAM in Heat benchmark

In this benchmark, we use the NAM memory to periodically save the computed matrix. The idea is to save the different states (snapshots) of the matrix during the execution in a persistent NAM memory region. Then, another program could retrieve all the matrix states, process them and produce a GIF animation showing the evolution of the heat during the whole execution. Notice that we cannot use simple RAM memory for that since the matrix could be huge and we may want to store tens of matrix snapshots. We also want the possibility of storing it in a persistent way, so other programs can process the stored data. Moreover, the memory should be easily accessible by the multiple MPI ranks or their tasks in parallel. The NAM memory fulfills all these conditions and ParaStationMPI allows accessing NAM regions through standard MPI RMA operations.

During the execution of the application and every few timesteps (specified by the user), the benchmark saves the whole matrix into a specific NAM subregion. Each timestep saving a matrix snapshot uses a distinct NAM subregion. These subregions are placed one after the other, consecutively, but without overlapping. Thus, the total size of the NAM region is the size of the whole matrix multiplied by the number of times the matrix will be saved. However, the NAM memory region is allocated using the Managed Contiguous layout (psnam_structure_managed_contiguous). This means that the rank 0 allocates the whole region but each rank acquires a consecutive memory subregion where it will store its blocks' data for all the spanshots. For instance, the NAM allocation will first have all the space for storing all snapshots of the blocks from rank 0, followed by the space for all snapshots of blocks from rank 1, and so on. Notice that the NAM subregions are addressed by the rank it belongs to, simplifying the task of saving and retrieving the snapshots.

When there is a timestep that requires a snapshot, the application instantiates multiple tasks that save the matrix data into the corresponding NAM subregion. Each MPI rank creates a task for saving the data of each matrix block into the NAM subregion. These communication tasks do not have any data dependency between them, so they can run in parallel writing data to the NAM region using regular MPI_Put. Ranks only write to the subregions that belong to themselves, never in other ranks' subregions. Even so, all MPI_Put calls must be done inside an RMA access epoch, so there must be one fence call before all the MPI_Put calls and another one after them to close the epoch for each of the timesteps with snapshot. Thus, here is where we use the new function MPI_Win_ifence together with the TAMPI non-blocking support. In this way, we taskify both synchronization and writing of NAM regions, keeping the data-flow model, and without having to stop the parallelism (e.g., with a taskwait) to perform the snapshots. Thanks to the task data dependencies and TAMPI, we cleanly include the snapshots in the application's data-flow execution as any other regular task.

The following pseudo-code shows how the saving of snapshots work in 02.heat_itampi_ompss2_tasks.bin: 

void solve() {
    int namSnapshotFreq = ...;
    int namSnapshotId = 0;

    for (t = 1; t <= timesteps; ++t) {
        // Computation and communication tasks declaring
        // dependencies on the blocks they process
        gaussSeidelSolver(...);

        if (t % namSnapshotFreq == 0) {
            namSaveMatrix(namSnapshotId, namWindow, ...);
            ++namSnapshotId;
        }
    }
    #pragma oss taskwait
}

void namSaveMatrix(int namSnapshotId, MPI_Win namWindow, ...) {
    // Compute destination offset in NAM region
    int snapshotOffset = namSnapshotId*sizeof(..all blocks..);

    // Open RMA access epoch to write the NAM window for this timestep
    #pragma oss task in(..all blocks..) inout(namWindow)
    {
        MPI_Request request;
        MPI_Win_ifence(namWindow, 0, &request);
        TAMPI_Iwait(&request, MPI_STATUS_IGNORE);
    }

    // Write all blocks from the current rank to NAM subregions concurrently
    for (B : all blocks) {
        #pragma oss task in(..block B..) in(namWindow)
        {
            MPI_Put(/* origin */ ..block B..,
                /* target rank */ currentRank,
                /* target offset */ snapshotOffset + B,
                /* target window */ namWindow);
        }
    }

    // Close RMA access epoch to write the NAM window for this timestep
    #pragma oss task in(..all blocks..) inout(namWindow)
    {
        MPI_Request request;
        MPI_Win_ifence(namWindow, 0, &request);
        TAMPI_Iwait(&request, MPI_STATUS_IGNORE);
    }
}

Requirements

The requirements of this application are shown in the following lists. The main requirements are:

Building & Executing on DEEP

The instructions to build and execute the Heat benchmark with NAM checkpointing will appear here soon.

References