About Jiri Kraus

Jiri Kraus is a developer in NVIDIA's European Developer Technology team. As a consultant for GPU HPC applications at the NVIDIA Jülich Applications Lab, Jiri collaborates with local developers and scientists at the Jülich Supercomputing Centre and the Forschungszentrum Jülich. Before joining NVIDIA Jiri worked on the parallelization and optimization of scientific and technical applications for clusters of multicore CPUs and GPUs at Fraunhofer SCAI in St. Augustin. He holds a Diploma in Mathematics from the University of Cologne, Germany.
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CUDA Pro Tip: Profiling MPI Applications

When I profile MPI+CUDA applications, sometimes performance issues only occur for certain MPI ranks. To fix these, it’s necessary to identify the MPI rank where the performance issue occurs. Before CUDA 6.5 it was hard to do this because the CUDA profiler only shows the PID of the processes and leaves the developer to figure out the mapping from PIDs to MPI ranks. Although the mapping can be done manually, for example for OpenMPI via the command-line option --display-map, it’s tedious and error prone. A solution which solves this for the command-line output of nvprof is described here http://www.parallel-computing.pro/index.php/9-cuda/5-sorting-cuda-profiler-output-of-the-mpi-cuda-program . In this post I will describe how the new output file naming of nvprof to be introduced with CUDA 6.5 can be used to conveniently analyze the performance of a MPI+CUDA application with nvprof and the NVIDIA Visual Profiler (nvvp).

Profiling MPI applications with nvprof and nvvp

Collecting data with nvprof

nvprof supports dumping the profile to a file which can be later imported into nvvp. To generate a profile for a MPI+CUDA application I simply start nvprof with the MPI launcher and up to CUDA 6 I used the string “%p” in the output file name. nvprof automatically replaces that string with the PID and generates a separate file for each MPI rank. With CUDA 6.5, the string “%q{ENV}” can be used to name the output file of nvprof. This allows us to include the MPI rank in the output file name by utilizing environment variables automatically set by the MPI launcher (mpirun or mpiexec). E.g. for OpenMPI OMPI_COMM_WORLD_RANK is set to the MPI rank for each launched process.

$ mpirun -np 2 nvprof -o simpleMPI.%q{OMPI_COMM_WORLD_RANK}.nvprof ./simpleMPI
Running on 2 nodes
==18811== NVPROF is profiling process 18811, command: ./simpleMPI
==18813== NVPROF is profiling process 18813, command: ./simpleMPI
Average of square roots is: 0.667279
PASSED
==18813== Generated result file: simpleMPI.1.nvprof
==18811== Generated result file: simpleMPI.0.nvprof

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Accelerating a C++ CFD code with OpenACC

Computational Fluid Dynamics (CFD) is a valuable tool to study the behavior of fluids. Today, many areas of engineering use CFD. For example, the automotive industry uses CFD to study airflow around cars, and to optimize the car body shapes to reduce drag and improve fuel efficiency. To get accurate results in fluid simulation it is necessary to capture complex phenomena such as turbulence, which requires very accurate models. These complex models result in very long computing times. In this post I describe how I used OpenACC to accelerate the ZFS C++ CFD solver with NVIDIA Tesla GPUs.

The ZFS flow solver

Engine
Figure 1: Using ZFS to study fluid flow within an internal combustion engine with moving pistons and valves.

The C++ flow solver ZFS (Zonal Flow Solver) is developed at the Institute of Aerodynamics at RWTH Aachen, Germany. ZFS solves the unsteady Navier-Stokes equations for compressible flows on automatically generated hierarchical Cartesian grids with a fully-conservative second-order-accurate finite-volume method [1, 2, 3]. To integrate the flow equations in time ZFS uses a 5-step Runge-Kutta method with dual time stepping [2]. It imposes boundary conditions using a ghost-cell method [4] that can handle multiple ghost cells [5, 6]. ZFS supports complex moving boundaries which are sharply discretized using a cut-cell type immersed-boundary method [1, 2, 7].

Among other topics, scientists have used ZFS to study the flow within an internal combustion engine with moving pistons and valves, as Figure 1 shows. Figure 2 shows how the Lattice-Boltzmann solver in ZFS was used to better understand airflow within the human nasal cavity.
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CUDA Pro Tip: Generate Custom Application Profile Timelines with NVTX

The last time you used the timeline feature in the NVIDIA Visual Profiler or NSight to analyze a complex application, you might have wished to see a bit more than just CUDA API calls and GPU kernels. Most applications do significant work on both the CPU and GPU, so it would be nice to see in more detail what CPU functions are taking time. This can help identify the sources of idle GPU time, for example.

In this post I will show you how you can use the NVIDIA Tools Extension (NVTX) to annotate the time line with useful information. I will demonstrate how to add time ranges by calling the NVTX API from your application or library. This can be a tedious task for complex applications with deeply nested call-graphs, so I will also explain how to use compiler instrumentation to automate this task.

What is the NVIDIA Tools Extension (NVTX)?

The NVIDIA Tools Extension (NVTX) is an application interface to the NVIDIA Profiling tools, including the NVIDIA Visual Profiler, NSight Eclipse Edition, and NSight Visual Studio Edition. NVTX allows you to annotate the profiler time line with events and ranges and to customize their appearance and assign names to resources such as CPU threads and devices.

Let’s use the following source code as the basis for our example. (This code is incomplete, but complete examples are available in the Parallel Forall Github repository.) Continue reading

Benchmarking CUDA-Aware MPI

I introduced CUDA-aware MPI in my last post, with an introduction to MPI and a description of the functionality and benefits of CUDA-aware MPI. In this post I will demonstrate the performance of MPI through both synthetic and realistic benchmarks.

Synthetic MPI Benchmark results

Since you now know why CUDA-aware MPI is more efficient from a theoretical perspective, let’s take a look at the results of MPI bandwidth and latency benchmarks. These benchmarks measure the run time for sending messages of increasing size from a buffer associated with MPI rank 0 to a buffer associated with MPI rank 1. Using MVAPICH2-1.9b I have measured the following bandwidths and latencies between two Tesla K20 GPUs installed in two nodes connected with FDR infiniband. I have included host-to-host MPI bandwidth results as a reference. The measured latencies for 1 byte messages are 19 microseconds for regular MPI, 18 microseconds for CUDA-aware MPI with GPUDirect accelerated communication with network and storage devices, and 1 microsecond for host-to-host communication. The peak bandwidths for the 3 cases are 6.19 GB/s for host-to-host transfers, 4.18 GB/s for device-to-device transfers with MVAPICH2-1.9b and GPUDirect, and 1.89 GB/s for device-to-device transfers with staging through host memory.

MPIbandwidth Continue reading

An Introduction to CUDA-Aware MPI

MPI, the Message Passing Interface, is a standard API for communicating data via messages between distributed processes that is commonly used in HPC to build applications that can scale to multi-node computer clusters. As such, MPI is fully compatible with CUDA, which is designed for parallel computing on a single computer or node. There are many reasons for wanting to combine the two parallel programming approaches of MPI and CUDA. A common reason is to enable solving problems with a data size too large to fit into the memory of a single GPU, or that would require an unreasonably long compute time on a single node. Another reason is to accelerate an existing MPI application with GPUs or to enable an existing single-node multi-GPU application to scale across multiple nodes. With CUDA-aware MPI these goals can be achieved easily and efficiently. In this post I will explain how CUDA-aware MPI works, why it is efficient, and how you can use it.

I will be presenting a talk on CUDA-Aware MPI at the GPU Technology Conference next Wednesday at 4:00 pm in room 230C, so come check it out!

A Very Brief Introduction to MPI

Before I explain what CUDA-aware MPI is all about, let’s quickly introduce MPI for readers who are not familiar with it. The processes involved in an MPI program have private address spaces, which allows an MPI program to run on a system with a distributed memory space, such as a cluster. The MPI standard defines a message-passing API which covers point-to-point messages as well as collective operations like reductions. The example below shows the source code of a very simple MPI program in C which sends the message “Hello, there” from process 0 to process 1. Note that in MPI a process is usually called a “rank”, as indicated by the call to MPI_Comm_rank() below.

#include <stdio.h>
#include <string.h>
#include <mpi.h>

int main(int argc, char *argv[])
{
    char message[20];
    int myrank, tag=99;
    MPI_Status status;

    /* Initialize the MPI library */
    MPI_Init(&argc, &argv);
    /* Determine unique id of the calling process of all processes participating
       in this MPI program. This id is usually called MPI rank. */
    MPI_Comm_rank(MPI_COMM_WORLD, &myrank);

    if (myrank == 0) {
        strcpy(message, "Hello, there");
        /* Send the message "Hello, there" from the process with rank 0 to the
           process with rank 1. */
        MPI_Send(message, strlen(message)+1, MPI_CHAR, 1, tag, MPI_COMM_WORLD);
    } else {
        /* Receive a message with a maximum length of 20 characters from process
           with rank 0. */
        MPI_Recv(message, 20, MPI_CHAR, 0, tag, MPI_COMM_WORLD, &status);
        printf("received %s\n", message);
    }

    /* Finalize the MPI library to free resources acquired by it. */
    MPI_Finalize();
    return 0;
}

This program can be compiled and linked with the compiler wrappers provided by the MPI implementation. Continue reading