ACES Phase I

To apply for an account on ACES Phase I please fill out the ACES Phase I application form

Hardware Summary

Component:	Quantity	Description
Graphcore IPU	16	16 Colossus GC200 IPUs and dual AMD Rome CPU server on a 100 GbE RoCE fabric
Intel FPGA PAC D5005	2	FPGA SOC with Intel Stratix 10 SX FPGAs, 64 bit quad-core Arm Cortex-A53 processors, and 32GB DDR4
Intel Optane SSDs	8	3 TB of Intel Optane SSDs addressable as memory using MemVerge Memory Machine.

Access

ssh access for XSEDE/ACCESS users

As of August 31st, login to ACES Phase I for XSEDE/ACCESS users transitioned from the XSEDE sso hub to a jump host:

ssh -J username@aces-jump.hprc.tamu.edu:8822 username@login.aces.hprc.tamu.edu

You can also find your username(s) under your ACCESS profile: https://allocations.access-ci.org/profile

ssh access for XSEDE/ACCESS mobaxterm users

MobaXterm offers the capability to use a jump host to connect to a server in a secured network zone. You can configure your SSH session to use a jump host to save time with the login process.

Click on the Session tab to create a new session. Next click SSH in the top ribbon.

Enter login.aces.hprc.tamu.edu into the Remote host * box.

Enter your username in the Specify username box.

Open the Advanced SSH Settings tab check the Use Private Key box. Then enter the path to your private key.

Go to the Network Settings tab and enable Connect through SSH gateway (jump host) and enter respective login data. The gateway host should be aces-jump.hprc.tamu.edu. The port should be 8822. Click OK.

Start the session by double clicking the session login.aces.hprc.tamu.edu (username) in the left hand ribbon or under the Sessions tab.

Note when creating a public key

Users using the jump host must use the default file name when creating a public key.

When ssh-keygen prompts you to enter a filename, press the enter (or return) key to accept the default file name.

Graphcore IPUs

From the login node, ssh into the poplar1 system.

[username@login ~]$ ssh poplar1

Set up the Poplar SDK environment

In this step, set up several environment variables to use the Graphcore tools and Poplar graph programming framework.

[username@poplar1 ~]$ source /opt/gc/poplar/poplar_sdk-ubuntu_18_04-[ver]/poplar-ubuntu_18_04-[ver]/enable.sh
[username@poplar1 ~]$ source /opt/gc/poplar/poplar_sdk-ubuntu_18_04-[ver]/popart-ubuntu_18_04-[ver]/enable.sh

[ver] indicates the version number of the package.

Example commands with an existing version on ACES:

source  /opt/gc/poplar/poplar_sdk-ubuntu_18_04-2.5.1+1001-64add8f33d/poplar-ubuntu_18_04-2.5.0+4748-e94d646535/enable.sh
source  /opt/gc/poplar/poplar_sdk-ubuntu_18_04-2.5.1+1001-64add8f33d/popart-ubuntu_18_04-2.5.1+4748-e94d646535/enable.sh

mkdir -p /localdata/$USER/tmp
export TF_POPLAR_FLAGS=--executable_cache_path=/localdata/$USER/tmp
export POPTORCH_CACHE_DIR=/localdata/$USER/tmp
# export POPLAR_LOG_LEVEL=INFO
# export POPLIBS_LOG_LEVEL=INFO

Set up environments of frameworks for IPU

PyTorch (Poptorch)

Set up PyTorch (Poptorch)

The local home dir is small (300G total). You can store large files in /localdata/username (or use localdata symlink from your home dir). /localdata has 3.5TB available.

[username@poplar1 ~]$ cd localdata
[username@poplar1 localdata]$ virtualenv -p python3 poptorch_test
[username@poplar1 localdata]$ source poptorch_test/bin/activate
[username@poplar1 localdata]$ python -m pip install -U pip
[username@poplar1 localdata]$ python -m pip install <sdk_path>/poptorch_x.x.x.whl

For <sdk_path>/poptorch_x.x.x.whl, you can use /opt/gc/poplar/poplar_sdk-ubuntu_18_04-2.5.1+1001-64add8f33d/poptorch-2.5.0+62288_0f4af0bf32_ubuntu_18_04-cp36-cp36m-linux_x86_64.whl, which exists on ACES

Clone a copy of the Graphcore tutorials repository and change the directory to mnist

[username@poplar1 localdata]$ git clone https://github.com/graphcore/tutorials.git
[username@poplar1 localdata]$ cd tutorials/simple_applications/pytorch/mnist/

Install the dependencies and run the model

[username@poplar1 mnist]$ pip install -r requirements.txt
[username@poplar1 mnist]$ python mnist_poptorch.py

TensorFlow 1

Set up TensorFlow 1 for IPU

The local home dir is small (300G total). You can store large files in /localdata/NetID (or use localdata symlink from your home dir). /localdata has 3.5TB available.

[username@poplar1 ~]$ cd localdata
[username@poplar1 localdata]$ virtualenv venv_tf1 -p python3.6
[username@poplar1 localdata]$ source venv_tf1/bin/activate
[username@poplar1 localdata]$ python -m pip install <sdk_path>/tensorflow_x.x.x.whl

For <sdk_path>/tensorflow_x.x.x.whl, you can use /opt/gc/poplar/poplar_sdk-ubuntu_18_04-2.5.1+1001-64add8f33d/tensorflow-1.15.5+gc2.5.1+193128+c9005c133f4+amd_znver1-cp36-cp36m-linux_x86_64.whl, which exists on ACES

Clone a copy of the Graphcore tutorials repository and change the directory to mnist

[username@poplar1 localdata]$ https://github.com/graphcore/tutorials.git
[username@poplar1 localdata]$ cd tutorials/simple_applications/tensorflow/mnist/

Run the model

[username@poplar1 localdata]$ python mnist.py

TensorFlow 2

Set up TensorFlow 2 for IPU

The local home dir is small (300G total). You can store large files in /localdata/NetID (or use localdata symlink from your home dir). /localdata has 3.5TB available.

[username@poplar1 ~]$ cd localdata
[username@poplar1 localdata]$ virtualenv venv_tf2 -p python3.6
[username@poplar1 localdata]$ source venv_tf2/bin/activate
[username@poplar1 localdata]$ python -m pip install <sdk_path>/tensorflow_x.x.x.whl

For <sdk_path>/tensorflow_x.x.x.whl, you can use /opt/gc/poplar/poplar_sdk-ubuntu_18_04-2.5.1+1001-64add8f33d/tensorflow-2.5.2+gc2.5.1+193132+4673d3afb3b+amd_znver1-cp36-cp36m-linux_x86_64.whl, which exists on ACES

Clone a copy of the Graphcore tutorials repository and change the directory to tensorflow2/keras/completed_demos

[username@poplar1 localdata]$ https://github.com/graphcore/tutorials.git
[username@poplar1 localdata]$ cd tutorials/tutorials/tensorflow2/keras/completed_demos/

Run the model

[username@poplar1 completed_demos]$ python completed_demo_ipu.py

Graphcore Documentation can be found at https://docs.graphcore.ai/en/latest/

Liqid PCIe Card with Intel Optane SSDs

Submit a standard batch job or interactive job to the memverge partition

srun --partition=memverge --time=24:00:00 --pty bash

Sample job file:

#!/bin/bash

##NECESSARY JOB SPECIFICATIONS
#SBATCH --job-name=Example           #Set the job name to Example
#SBATCH --time=24:00:00              #Set the wall clock limit to 24 hrs
#SBATCH --nodes=1                    #Request 1 nodes
#SBATCH --ntasks-per-node=64         #Request 64 tasks/cores per node
#SBATCH --mem=248G                   #Request 248G (248GB) per node
#SBATCH --output=Example.%j          #Redirect stdout/err to file
#SBATCH --partition=memverge         #Specify the MemVerge partition

#lines required to setup the environment for your code

# add the mm command in front of your executable to run with memory machine
mm executable

Sample job file to run with singularity

#!/bin/bash

##NECESSARY JOB SPECIFICATIONS
#SBATCH --job-name=Example           #Set the job name to Example
#SBATCH --time=24:00:00              #Set the wall clock limit to 24 hrs
#SBATCH --nodes=1                    #Request 1 nodes
#SBATCH --ntasks-per-node=64         #Request 64 tasks/cores per node
#SBATCH --mem=248G                   #Request 248G (248GB) per node
#SBATCH --output=Example.%j          #Redirect stdout/err to file
#SBATCH --partition=memverge         #Specify the MemVerge partitionexport SINGULARITY_BIND='/var/log/memverge,/etc/memverge,/opt/memverge,/var/memverge'

# Required directories and libraries for memverge memory machine
export SINGULARITY_BIND='/var/log/memverge,/etc/memverge,/opt/memverge,/var/memverge'

for lib in \
libblkid.so.1 \
libcrypto.so.1.1 \
libc.so.6 \
libdaxctl.so.1 \
libdl.so.2 \
libgcc_s.so.1 \
libkmod.so.2 \
liblzma.so.5 \
libmount.so.1 \
libm.so.6 \
libndctl.so.6 \
libpcre2-8.so.0 \
libprotobuf-c.so.1 \
libpthread.so.0 \
librt.so.1 \
libselinux.so.1 \
libssl.so.1.1 \
libstdc++.so.6 \
libudev.so.1 \
libuuid.so.1 \
libz.so.1 \
; do export  SINGULARITY_BIND=$SINGULARITY_BIND,/lib64/$lib:/lib/$lib ; done

# run your singularity container command, included the mm command for memverge memory machine
singularity exec filename.sif mm executable

Intel FPGA PAC D5005

The FPGA nodes support both an older OpenCL development workflow, as well as a newer Intel oneAPI workflow. FPGA code compilation with

Access

⚠️ It is recommended to compile an FPGA binary on a CPU node, as the compilation times are extensive for hardware images, and the availability of FPGA nodes is limited. As such, Texas A&M High Performance Research Computing has created a Singularity container available for performing this compilation on CPU-only nodes. After compilation, users may request an FPGA node to run their emulator/hardware image.

CPU-Only (For Compiling Binaries)

On the login node: Create a copy of the container and samples:

cp -R /scratch/training/oneapi-fpga $SCRATCH

Launch an interactive job on a CPU node:

srun --partition=cpu --nodes=1 --mem=64G --time=12:00:00 --pty bash -i

Start an interactive session in the Singularity container

cd $SCRATCH/oneapi-aces
singularity shell --env-file env-vars oneapi-2022.1.0.sif

Follow the steps in the next section, "Getting Started".

FPGA (For Running Binaries)

To access the Intel FPGA PAC D5005, submit an interactive job to the FPGA partition from the login node:

srun --partition=fpga --nodes=1 --time=24:00:00 --pty bash -i

Getting Started

Once the session starts, you need to load the environment variables to access and interact with the FPGA on the node:

$ source /opt/intel/oneapi/setvars.sh
    :: initializing oneAPI environment ...
       -bash: BASH_VERSION = 4.2.46(2)-release
       args: Using "$@" for setvars.sh arguments: 
    :: advisor -- latest
    :: ccl -- latest
    :: compiler -- latest
    :: dal -- latest
    :: debugger -- latest
    :: dev-utilities -- latest
    :: dnnl -- latest
    :: dpcpp-ct -- latest
    :: dpl -- latest
    :: intelfpgadpcpp -- latest
    :: intelpython -- latest
    :: ipp -- latest
    :: ippcp -- latest
    :: ipp -- latest
    :: mkl -- latest
    :: mpi -- latest
    :: tbb -- latest
    :: vpl -- latest
    :: vtune -- latest
    :: oneAPI environment initialized ::

Telemetry for the FPGA can be viewed using the 'fpgainfo' command:

$ fpgainfo
    FPGA information utility
    Usage:
           fpgainfo [-h] [-B <bus>] [-D <device>] [-F <function>] [-S <socket-id>] {errors,power,temp,fme,port,bmc}
                   -h,--help           Print this help
                   -B,--bus            Set target bus number
                   -D,--device         Set target device number
                   -F,--function       Set target function number
                   -S,--socket-id      Set target socket number
    Subcommands:
    Print and clear errors
           fpgainfo errors [-h] [-c] {all,fme,port}
                   -h,--help           Print this help
                   -c,--clear          Clear all errors
                   --force             Retry clearing errors 64 times
                                       to clear certain error conditions
    Print power metrics
           fpgainfo power [-h]
                   -h,--help           Print this help
    Print thermal metrics
           fpgainfo temp [-h]
                   -h,--help           Print this help
    Print FME information
           fpgainfo fme [-h]
                   -h,--help           Print this help
    Print accelerator port information
           fpgainfo port [-h]
                   -h,--help           Print this help
    Print all Board Management Controller sensor values
           fpgainfo bmc [-h]
                   -h,--help           Print this help

For continuous monitoring, utilize this command in conjunction with the 'watch' command.

To run a status check on the FPGA, run:

$ aocl diagnose

This will display information about the libraries and initialization status of the FPGA device.

If the device shows as "Unitialized", it can be initialized with a standard image with:

$ aocl initialize acl0 pac_s10
    aocl initialize: Running initialize from /opt/intel/oneapi/intelfpgadpcpp/latest/board/intel_s10sx_pac/linux64/libexec
    Program succeed.

The FPGA device must be initialized with the image that matches the compilation target of a binary i.e. if a binary is compiled for "pac_s10", the board must be initialized with the "pac_s10" standard image before running. There are two potential image options for the "aocl initialize" command:

Name	Description
pac_s10	Standard Intel FPGA PAC D5005 (Intel Stratix 10 SX) without unified shared memory support (USM).
pac_s10_usm	Standard Intel FPGA PAC D5005 (Intel Stratix 10 SX) with unified shared memory support (USM). Device must be initialized with this image if a binary using USM will be run on the FPGA device.

More information regarding unified shared memory can be found here: Unified Shared Memory — DPC++ Reference documentation

If the node has multiple FPGA devices, they can be viewed with:

$ aocl list-devices
    --------------------------------------------------------------------
    Device Name:
    acl0
    
    BSP Install Location:
    /opt/intel/oneapi/intelfpgadpcpp/latest/board/intel_s10sx_pac
    
    Vendor: Intel Corp
    
    Physical Dev Name   Status            Information
    
    pac_ed00000         Passed            Intel PAC Platform (pac_ed00000)
                                          PCIe 29:00.0
                                          USM not supported
    
    DIAGNOSTIC_PASSED
    --------------------------------------------------------------------

The user can then target the correct device when running their code or initializing the device.

Example

oneAPI Samples

The README.md in each directory contains information for compiling and running.

$ git clone https://github.com/oneapi-src/oneAPI-samples.git
$ cd oneAPI-samples/DirectProgramming/DPC++FPGA/Tutorials

Example 1: fpga_compile

Navigate to the "fpga_compile" example under "GettingStarted" within the oneAPI samples repository:

$ cd GettingStarted
$ cd fpga_compile

Create a build directory for configuration files:

$ mkdir build
$ cd build

Configure the program to compile for the Intel S10 SX PAC (Intel PAC D5005):

$ cmake .. -DFPGA_DEVICE=intel_s10sx_pac:pac_s10

Once the configuration completes, there will be several make options available:

Compilation Types
Command	Device Image Type	Compilation Duration	Description
make fpga_emu	FPGA Emulator	Seconds	Compile for emulation (compiles quickly, targets emulated FPGA device). Allows user to validate design, but does not represent actual performance of code on hardware.
make report	Optimization Report	Minutes	Generate the optimization report. The FPGA device code is partially compiled for hardware. The compiler generates an optimization report that describes the structures generated on the FPGA, identifies performance bottlenecks, and estimates resource utilization.
make fpga	FPGA Hardware	Hours	Compile for FPGA hardware (takes longer to compile, targets FPGA device). Compiles the actual bitstream for running the program on hardware.

The recommended workflow is to compile a program for emulation prior to compiling for hardware execution. This does not actually compile the program to run on the FPGA itself, but rather on the CPU via a virtual FPGA emulation device. This allows a user to validate the correctness of their design while benefiting from the short compile times of CPU compilation. The optimization report assists the user in improving different aspects of their design before moving onto hardware compilation.

Example 2: buffered_host_streaming

Navigate to the "buffered_host_streaming" example under "DesignPatterns" within the oneAPI samples repository:

$ cd DesignPatterns
$ cd buffered_host_streaming

Create a build directory for configuration files:

$ mkdir build
$ cd build

Configure the program to compile for the Intel S10 SX PAC (Intel PAC D5005):

$ cmake .. -DFPGA_DEVICE=intel_s10sx_pac:pac_s10_usm -DUSM_HOST_ALLOCATIONS_ENABLED=1

Note that the value of FPGA_BOARD is the USM variant of the FPGA device. Like in Example 1, there will be three make targets to choose from: fpga_emu, report, and fpga. Once compilation finishes, ensure that the board is initialized to the correct standard image:

$ aocl initialize acl0 pac_s10_usm
$ aocl list-devices
    --------------------------------------------------------------------
    Device Name:
    acl0
    
    BSP Install Location:
    /opt/intel/oneapi/intelfpgadpcpp/latest/board/intel_s10sx_pac
    
    Vendor: Intel Corp
    
    Physical Dev Name   Status            Information
    
    pac_ed00000         Passed            Intel PAC Platform (pac_ed00000)
                                          PCIe 29:00.0
                                          USM supported
    
    DIAGNOSTIC_PASSED
    --------------------------------------------------------------------

Example output:

# buffered_host_streaming.fpga
$ ./buffered_host_streaming.fpga
    Repetitions:      200
    Buffers:          2
    Buffer Count:     524288
    Iterations:       4
    Total Threads:    64
    
    Running the roofline analysis
    Producer (32 threads)
            Time:       1.1101 ms
            Throughput: 30226.1777 MB/s
    Consumer (32 threads)
            Time:       1.0272 ms
            Throughput: 32667.0989 MB/s
    Producer & Consumer (32 threads, each)
            Time:       3.4327 ms
            Throughput: 9774.9486 MB/s
    Kernel
            Time:       3.5139 ms
            Throughput: 9549.1001 MB/s
    
    Maximum Design Throughput: 9549.1001 MB/s
    The FPGA kernel limits the performance of the design
    Done the roofline analysis
    
    Running the full design without API
    Average latency without API: 4.3190 ms
    Average processing time without API: 749.3281 ms
    Average throughput without API: 8955.8717 MB/s
    
    Running the full design with API
    Average latency with API: 4.6629 ms
    Average processing time with API: 1005.6579 ms
    Average throughput with API: 6673.1306 MB/s
    
    PASSED

Example output if the incorrect standard board image is programmed:

$ aocl inititalize acl0 pac_s10    # incorrect board; binaries compiled with pac_s10_usm
$ ./buffered_host_streaming.fpga
    Repetitions:      200
    Buffers:          2
    Buffer Count:     524288
    Iterations:       4
    Total Threads:    64
    
    ERROR: The selected device does not support USM host allocations
    terminate called without an active exception
    Aborted (core dumped)

Resources

Resource	Description
FPGA Optimization Guide for Intel® oneAPI Toolkits	The FPGA Optimization Guide for Intel® oneAPI Toolkits provides guidance on leveraging the functionalities of SYCL* to optimize a design.
Intel® FPGA Programmable Acceleration Card D5005 Data Sheet	This datasheet for the Intel® FPGA PAC shows electrical, mechanical, compliance, and other key specifications. This datasheet assists data center operators and system integrators to properly deploy the Intel® FPGA PAC into their servers. It also documents the FPGA power envelope, connectivity speeds to memory, and network connectivity, so that accelerator function unit (AFU) developers can properly design and test their IP.
Intel® FPGA Training	Set of labs for using FPGAs with oneAPI through the Intel® DevCloud.
Intel® Quartus® Prime Pro Edition User Guide: Scripting	Detailed guide for running Quartus programs on the command line.
Intel® Stratix® 10 FPGAs & SoC FPGA	Assorted documentation for the Stratix 10 FPGA family, including pinouts and device schematics.
Intel® Stratix® 10 FPGA Developer Center	Provides various resources to complete an Intel® FPGA design on the Stratix 10 architecture.
Why is FPGA Compilation Different?	Describes differences between CPU, GPU, and FPGA program compilation.

Support

Please report any issues encountered on the FPGAs to help@hprc.tamu.edu, and include information about actions taken and/or commands run prior to the error so the HPRC team may reproduce and resolve the issue.

Intel GPU

Intel GPU access is by invitation only.

Sample job file:

#!/bin/bash

##NECESSARY JOB SPECIFICATIONS
#SBATCH --job-name=Example           #Set the job name to Example
#SBATCH --time=24:00:00              #Set the wall clock limit to 24 hrs
#SBATCH --nodes=1                    #Request 1 nodes
#SBATCH --ntasks-per-node=64         #Request 64 tasks/cores per node
#SBATCH --mem=248G                   #Request 248G (248GB) per node
#SBATCH --output=Example.%j          #Redirect stdout/err to file
#SBATCH --partition=atsp             #Specify the Intel GPU

#lines required to setup the environment for your code

Command to activate Intel oneAPI

source /sw/restricted/oneapi_nda/setvars.sh 

:: initializing oneAPI environment ...
   -bash: BASH_VERSION = 4.4.20(1)-release
   args: Using "$@" for setvars.sh arguments: 
:: advisor -- latest
:: ccl -- latest
:: clck -- latest
:: compiler -- latest
:: dal -- latest
:: debugger -- latest
:: dev-utilities -- latest
:: dnnl -- latest
:: dpcpp-ct -- latest
:: dpl -- latest
:: inspector -- latest
:: intelpython -- latest
:: ipp -- latest
:: ippcp -- latest
:: ipp -- latest
:: itac -- latest
:: mkl -- latest
:: mpi -- latest
:: neural-compressor -- latest
:: pytorch -- latest
:: tbb -- latest
:: tensorflow -- latest
:: vpl -- latest
:: vtune -- latest
:: oneAPI environment initialized ::

Command to activate a specific oneAPI environment:

source /sw/restricted/oneapi_nda/setvars.sh --force source activate env

For example, to load the oneAPI intel optimized python environment run:

source /sw/restricted/oneapi_nda/setvars.sh --force source activate intelpython

ACES

Contents

ACES Phase I

Hardware Summary

Access

ssh access for XSEDE/ACCESS users

ssh access for XSEDE/ACCESS mobaxterm users

Note when creating a public key

Graphcore IPUs

Set up the Poplar SDK environment

Set up environments of frameworks for IPU

PyTorch (Poptorch)

Set up PyTorch (Poptorch)

Clone a copy of the Graphcore tutorials repository and change the directory to mnist

Install the dependencies and run the model

TensorFlow 1

Set up TensorFlow 1 for IPU

Clone a copy of the Graphcore tutorials repository and change the directory to mnist

Run the model

TensorFlow 2

Set up TensorFlow 2 for IPU

Clone a copy of the Graphcore tutorials repository and change the directory to tensorflow2/keras/completed_demos

Run the model

Liqid PCIe Card with Intel Optane SSDs

Intel FPGA PAC D5005

Access

CPU-Only (For Compiling Binaries)

FPGA (For Running Binaries)

Getting Started

Example

oneAPI Samples

Example 1: fpga_compile

Example 2: buffered_host_streaming

Resources

Support

Intel GPU

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