Clarence's Wicked Mind

Saturday, January 11, 2020

ROCm with Ryzen 2200G

For reference, here is the output from running /opt/rocm/bin/rocminfo on Ryzen 2200G (with vega 8) after installing ROCm:

ROCk module is loaded
userx is member of video group
=====================
HSA System Attributes
=====================
Runtime Version:         1.1
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model:           LARGE
System Endianness:       LITTLE

==========
HSA Agents
==========
*******
Agent 1
*******
Name:                    AMD Ryzen 3 2200G with Radeon Vega Graphics
Marketing Name:          AMD Ryzen 3 2200G with Radeon Vega Graphics
Vendor Name:             CPU
Feature:                 None specified
Profile:                 FULL_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        0(0x0)
Queue Min Size:          0(0x0)
Queue Max Size:          0(0x0)
Queue Type:              MULTI
Node:                    0
Device Type:             CPU
Cache Info:
    L1:                      32(0x20) KB
Chip ID:                 5597(0x15dd)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   3500
BDFID:                   1792
Internal Node ID:        0
Compute Unit:            4
SIMDs per CU:            4
Shader Engines:          1
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Features:                None
Pool Info:
    Pool 1
      Segment:                 GLOBAL; FLAGS: KERNARG, FINE GRAINED
      Size:                    16776832(0xfffe80) KB
      Allocatable:             TRUE
      Alloc Granule:           4KB
      Alloc Alignment:         4KB
      Acessible by all:        TRUE
ISA Info:
    N/A
*******
Agent 2
*******
Name:                    gfx902
Marketing Name:          AMD Ryzen 3 2200G with Radeon Vega Graphics
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 FULL_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          4096(0x1000)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    0
Device Type:             GPU
Cache Info:
    L1:                      16(0x10) KB
Chip ID:                 5597(0x15dd)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1100
BDFID:                   1792
Internal Node ID:        0
Compute Unit:            11
SIMDs per CU:            4
Shader Engines:          1
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Features:                KERNEL_DISPATCH
Fast F16 Operation:      FALSE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
    x                        1024(0x400)
    y                        1024(0x400)
    z                        1024(0x400)
Max Waves Per CU:        160(0xa0)
Max Work-item Per CU:    10240(0x2800)
Grid Max Size:           4294967295(0xffffffff)
Grid Max Size per Dimension:
    x                        4294967295(0xffffffff)
    y                        4294967295(0xffffffff)
    z                        4294967295(0xffffffff)
Max fbarriers/Workgrp:   32
Pool Info:
    Pool 1
      Segment:                 GROUP
      Size:                    64(0x40) KB
      Allocatable:             FALSE
      Alloc Granule:           0KB
      Alloc Alignment:         0KB
      Acessible by all:        FALSE
ISA Info:
    ISA 1
      Name:                    amdgcn-amd-amdhsa--gfx902+xnack
      Machine Models:          HSA_MACHINE_MODEL_LARGE
      Profiles:                HSA_PROFILE_BASE
      Default Rounding Mode:   NEAR
      Default Rounding Mode:   NEAR
      Fast f16:                TRUE
      Workgroup Max Size:      1024(0x400)
      Workgroup Max Size per Dimension:
        x                        1024(0x400)
        y                        1024(0x400)
        z                        1024(0x400)
      Grid Max Size:           4294967295(0xffffffff)
      Grid Max Size per Dimension:
        x                        4294967295(0xffffffff)
        y                        4294967295(0xffffffff)
        z                        4294967295(0xffffffff)
      FBarrier Max Size:       32
*** Done ***

Wednesday, November 13, 2019

Compiling cloudflared for armv6

I have a Raspberry Pi 1 running pi-hole for ads filtering. Recently I tried to configure it to use Cloudflare DNS-over-https but it isn't stable. Not to mention that the latest "official" release of cloudflared is broken for Raspberry Pi. So here are the steps to compile it on Raspberry Pi (Note that the build process will take 30+ minutes on RPi. So you may want to cross-compile it on a PC instead. Just change the env variable CGO_ENABLED to 0 in the build script. It only takes a few seconds to compile on my Ryzen 2200G. Compiling it on RPi is just for fun).

Preparation

We need golang 1.12 or later to compile cloudflared. But the official go version on Raspbian is 1.7. Use this to download golang 1.12. Extract the content and take note of the path.

wget https://storage.googleapis.com/golang/go1.12.linux-armv6l.tar.gz

Also, the build process needs more than 1GB of memory. You may want to create a temporary swap file:

sudo fallocate -l 1G swapfile
sudo chmod 600 swapfile
sudo mkswap swapfile
sudo swapon swapfile

After building cloudflared, you can use "swapoff" to remove the temporary swap space and then delete the physical file.

Compiling

Here is the script to compile the cloudflared binary. Save and run it under a working folder. Edit the definition of the first two variables to point to the golang version you are using and the cloudflared version to compile. If you are corss-compiling, change the "CC" environment to the cross compiler.

#!/bin/sh
set -e

# custom install version of go >= 1.12
GOLANG_PATH=$HOME/apps/go/bin
# which version to build
CLOUDFLARED_VERSION=2019.11.0

export GOPATH=$(pwd)
export GOOS=linux
export GOARCH=arm
export GOARM=6
export CGO_ENABLED=1
export CC=gcc
export PATH=${GOLANG_PATH}:$PATH
CLOUDFLARED_BUILDTIME=$(date)

go get -v github.com/cloudflare/cloudflared/cmd/cloudflared
cd src/github.com/cloudflare/cloudflared
git checkout tags/${CLOUDFLARED_VERSION}
cd ../../../../

go build -v "-ldflags=-X 'main.Version=${CLOUDFLARED_VERSION}' -X 'main.BuildTime=${CLOUDFLARED_BUILDTIME}'" github.com/cloudflare/cloudflared/cmd/cloudflared

Installation

Follow the instructions on pi-hole web site on how to configure pi-hole to use DNS-over-https

Saturday, May 4, 2019

Building Visual Studio Code on Jetson Nano

Here are the steps for building Visual Studio Code on Nvidia Jetson Nano.

Install nodejs

Visual Studio Code needs node version between 8 and 10. To install node 10 and yarn:

curl -sL https://deb.nodesource.com/setup_10.x | sudo -E bash -

sudo apt-get install -y nodejs

curl -sL https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -

echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list

sudo apt-get update && sudo apt-get install yarn

Install libraries

We also need some libraries:

sudo apt-get install libx11-dev libxkbfile-dev libsecret-1-dev

Clone and build Visual Studio Code

In a working directory, clone the source code and build.

git clone https://github.com/microsoft/vscode

cd vscode

scripts/npm.sh install

Edit 2019-06-15: To enable extensions, edit the product.json file and add the following:

"extensionsGallery": {
  "serviceUrl": "https://marketplace.visualstudio.com/_apis/public/gallery",
  "cacheUrl": "https://vscode.blob.core.windows.net/gallery/index",
  "itemUrl": "https://marketplace.visualstudio.com/items"
}

To run Visual Studio Code:

scripts/code.sh

Friday, May 3, 2019

ROCm OpenCL with openSUSE Tumbleweed

EDIT 20200110:
~~(0) As of 2020-01-05, running "clinfo" with openSUSE Tumbleeweed 20200103 and these ROCm packages will cause null pointer dereference errors.~~
(0) Things are working again with Tumbleeweed 20200108.
(1) The user needs to be member of the "video" group. Run this command to add yourself to the group "sudo usermod -a -G video $LOGNAME"
(2) The site now has RPM packages for openSUSE. Go the http://repo.radeon.com/rocm/zyp/zypper/ to download the packages instead. Also, no need to specify "--nodeps" when installing the packages

- - 8< - - -

With Tumbleweed running Linux kernel 5.0, it is easy to install ROCm OpenCL with upstream kernel.

- Go to http://repo.radeon.com/rocm/zyp/zypper/ and download the latest version of following packages:

hsakmt-roct
hsa-rocr-dev
rocm-opencl
rocm-opencl-devel
rocminfo-1.0.0

- Install them without checking dependencies, assuming you already have libc etc packages installed, e.g.:

sudo rpm -ivh hsakmt-roct-1.0.9-135-g34da614-Linux.rpm hsa-rocr-dev-1.1.9-64-g619177ee-Linux.rpm rocm-opencl-1.2.0-2019040803.x86_64.rpm rocm-opencl-devel-1.2.0-2019040803.x86_64.rpm rocminfo-1.0.0-Linux.rpm

- That's it! Optionally, install clinfo with zypper to check the available platform. If you have been using Mesa OpenCL, you may want to uninstall it.

Saturday, April 20, 2019

OpenCL hangs with mesa-opencl and Radeon

OpenCL applications (e.g. clinfo and gimp etc) will hang when running under openSUSE Tumbleweed. This is with Mesa-libOpenCL 19.0.1 running on Radeon Sea Islands with amdgpu driver (radeon.cik_support=0 amdgpu.cik_support=1 amdgpu.dc=1).

Seems hitting this same bug.

EDIT: Fixed in Mesa 19.0.3.

Monday, March 25, 2019

NVIDIA Jetson Nano first impression

Just received my NVIDIA Jetson Nano developer kit today. Here are my first impressions:

- the mounting holes on the board are tiny. M3 pole won't fit. The package does come with a paper (!) stand though

- the board is picky on the power supply. Tried several USB chargers and the board will shutdown during boot. Ended up using the barrel plug. Needed to short jumper J48 to enable power supply via the barrel plug.

- during first boot, after configuring the keyboard etc, the machine froze when trying to login. Needed to recycle the power.

- Need to manually install Tensorflow for python3 (this should install the CUDA accelerated Tensorflow)

- When trying to install scipy with pip3 (compiling from source), the heatsink was too hot to touch and the machine eventually locked up. Needed to cycle the power to reboot.

- the cpuinfo:

- dmesg:

- lsusb (with keyboard and mouse attached):

- first successful login:

Sunday, February 24, 2019

Windows 7 update error 0x8000FFFF

Just found that my Windows 7 box failed to install the monthly quality rollup since Sep 2018.

There are many discussions on the error code 8000FFFF. From modifying registry to restarting services.

But in my case, it turns out that the monthly rollups depend on another package KB3177467. Once manually install KB3177467, the latest monthly rollup can be installed successfully.

Microsoft, please learn to set up dependencies.

Sunday, January 27, 2019

Playing with Google Apps Script

Long time no post!

Here is a little piece of Javascript code that I recently wrote on a Sunday evening. To be exact, it is a Google Apps Script.

The script processes credit card purchase notifications in my Gmail inbox and logs the data on a Google Sheet. From there, I could further analyze my spending.

First time using Google Apps Script. Seems quite powerful and a handy tool for automation.

Sunday, October 7, 2018

A GUI desktop application to read export files from AndSafe

From time to time, there are people contacting me asking about how they can read the export files from AndSafe.

So here it is. A quick hack put together with PyQt5: https://github.com/kitsook/PyAndSafe.

Monday, June 4, 2018

Measuring film camera shutter speed with Beaglebone Black

Here is a simple circuit utilizing the ADC input on Beaglebone Black to measure camera shutter speed.

A photoresistor is used to detect the light when shutter open, and hence changing the values read by ADC pin P9_40. The push button is used to trigger the Python script to start reading the ADC input.

Testing on breadboard and the final build on through-hole board:

Note that this is by no mean an accurate measure of absolute shutter speed. The response time (rise/fall) of photoresistor will affect the result (see the graph below). Nevertheless, it is useful to compare different shutter speeds.

Source code is available on Github.

Quick start:

git clone https://github.com/kitsook/bbb-adc-measure
cd bbb-adc-measure
python3 -m venv venv
. venv/bin/activate
pip install -r requirements.txt
python adc-measure.py -t 10 > result.csv

Plot the output using gnuplot:

gnuplot
set mxtics 10
set grid
plot 'result.csv' with lines

Here is an example output of measuring 1/15 and 1/8 shutter speeds of my Pentax MX.