1. Overview

General introduciton to initial GPE project can be found here.

2. GPU Hardware

The current GPU system at DESY (Zeuthen) for the GPE project consists of two servers with dual nVidia Tesla C2050 GPU cards. They are named gpu1 and gpu2 and are also used as a testbed for new developments in GPU-to-GPU networking with custom designed interconnects and InfiniBand.

3. Environment

Currently on the systems the newest version of the CUDA SDK 8.0 alongside with device drivers and libraries are installed. The Software Development Kit provides the following:

4. GPU Benchmarks

For evaluation and development a set of common benchmarks, as well as specially designed micro benchmarks were run on the gpu1/2 systems.

4.1. Custom low-level benchmarks

Custom designed benchmarks use OpenMPI and OpenMP for task parallelization and allocation on the host CPUs and evaluate the following performance metrics discussed below. For allocating benchmark threads to particular processor socets and cores specific options to mpirun were used. These include OpenMPI processor/memory affinity options described below:

mpirun --mca mpi_paffinity_alone 1 --mca rmaps_rank_file_path rank.cfg -np 4 executable

The option mpi_paffinity_alone=1 enables processor (and potentially memory) affinity. The options shown can be also defined via environment variables:

export OMPI_MCA_mpi_paffinity_alone=1
export OMPI_MCA_rmaps_rank_file_path=rank.cfg

#1: 1x2 cores / 2x processes / 1x thread per process
rank 0=znpnb90 slot=0
rank 1=znpnb90 slot=1

#2: 1x4 cores / 2x processes / 2x threads per process
rank 0=gpu1 slot=0-1
rank 1=gpu1 slot=2-3

#3: 2x4 cores / 2x processes / 4x threads per process
rank 0=gpu1 slot=0:*
rank 1=gpu1 slot=1:*

Using this a couple of benchmarks were implemented and run. These measure bandwidth of host do device memory transactions, as well as latencies of memory transactions for the case where two GPUs work simultaneously.

  1. Memory Bandwidth for unpinned memory and synchronous / asynchronous transfers
    • Here the bandwidth of host to device memory copy operations is measured. The host memory areas used are allocated with common malloc() calls and are not pinned to physical page addresses - thus they are subject to page swapping. On the other hand memory is pinned when allocating it via the cudaHostAlloc() call. For differentiating between synchronous and asynchronous transfers we used cudaMemcpy and cudaMemcpyasync. The effects of both transfer type and memory pinning are scribed here.

  2. Latency of host-ot-GPU memory copy operations for mulltiple GPUs
    • Here the latency for host to device memory copy operations is measured. This time however the memory regions of the host memory are pinned to physical addresses and only asynchronous memory transfer is used. The difference in the setup here is that both GPUs run the benchmark simultaneously, and we differe between two configurations - parallel (host process running on CPU socket 0 uses GPU 0, and process running on CPU socket 1 uses GPU 1) and cross (process on CPU socet 0 uses GPU 1 and vice versa).
    • Latecny of host-to-GPU memory copy operations for parallel configuration Measurement with RDTSC for process with rank 0 and two GPUs working "parallel"

    • Latency of host-to-GPU memory copy operations for corss configuration Measurement with RDTSC for process with rank 0 and two GPUs working "crossed"

  3. Bandwidth and latency of GPU-to-GPU communication
    1. MPI send/recv vs. CUDA 4.0 peer-to-peer communication primitives
  4. GPU-to-InfiniBand hardware datapath propagation delay

    • For measuring the hardware datapath latency between host CPU and InfiniBand network adapter, as well as beteen GPU and InfiniBand network adapter, a micro benchmarks was developed which utilizes the loopback capability of the INfiniband HCA. In such mode packets send from a process running on the host CPU are looped back "inwards".

    • {X} For the current one-server setup such measurements are not possible however, because of the limitation the used QSFP loopback connector poses - if the HCA is not directly connected to other HCA or infiniBand switch, then the subnet manager throws an error for duplicate addresses.

4.2. Application-level benchmark suites

For ensuring consistency of performance with real-world applications, the Scalable Heter Ogeneous Computing (SHOC) benchmark suite was run on the gpu1 system too. This benchmark suite gives not only benchmark results for CUDA implementations of key algorithms but also corresponding implementations of the more general OpenCL parallel programming framework.

The SHOC benchmark suite currently contains benchmark programs, categoried based on complexity. Some measure low-level "feeds and speeds" behavior (Level 0), some measure the performance of a higher-level operation such as a Fast Fourier Transform (FFT) (Level 1), and the others measure real application kernels (Level 2).

4.3. GPUDirect

<!> The term GPUDirect refers to a mechanism which allows different drivers to share pinned memory pages. This is realised by a (minor) modification of the Linux kernel and change in the network device drivers (i.e. IB device driver). The pages are pinned via the CUDA driver and the drivers who want to use these pages have to implement a mechanism which allow these to be notified about any change.

5. GPU Applications

<!> Recent applications utilizing the gpu1 system at DESY (Zeuthen) are Chroma-based LQCD munerical simulationd and applications from the field of Astro Particle Physics.

5.1. Debugger and Profiler Tools


6. Monitoring


7. New GPU server


An example specification for a second GPU server can be as follows:

Optional components:

8. Success/failure stories

Peer-to-Peer GPU memory access restrictions on dual Intel IOH machines

NOTE: Sections marked with <!> need further discussion or are work in progress

GPU (last edited 2017-05-15 09:10:53 by GötzWaschk)