Ilmu Pengetahuan & Teknologi - Laman 14 dari 38

Instalasi CUDA driver untuk GPU NVidia di Ubuntu 20.04

Berikut ini prosedur instalasi CUDA driver untuk Nvidia cards di Ubuntu 20.04

CUDA driver ini akan melakukan instalasi GUI, jadi sebaiknya kita pakai Ubuntu Desktop, atau kalau menggunakan Ubuntu server yang belum ada GUInya, install dulu GUI sederhana untuk Ubuntu, misalnya dari artikel “How to Install a Desktop (GUI) on an Ubuntu Server“

Tahap selanjutnya adalah mengikuti prosedur instalasi di artikel “CUDA Toolkit 11.4 Downloads“. Pada laman tersebut kita masukkan saja platform kita. Prosedur instalasi berbeda-beda untuk masing-masing platform.

Sebagai contoh, berikut ini pilihan platform saya:

Untuk Ubuntu, pada saat tulisan ini dibuat hanya dapat dilakukan instalasi di Ubuntu versi 18.04 dan 20.04

Berikut ini prosedur instalasi yang ditampilkan berdasarkan pilihan di atas:

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.4.0/local_installers/cuda-repo-ubuntu2004-11-4-local_11.4.0-470.42.01-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004-11-4-local_11.4.0-470.42.01-1_amd64.deb
sudo apt-key add /var/cuda-repo-ubuntu2004-11-4-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda

Prosedur tersebut dapat dilakukan apa adanya tanpa perubahan. Jika kita melakukan instalasi di beberapa server yang berbeda, proses download dengan wget cukup dilakukan sekali saja, untuk instalasi berikutnya file *.deb tersebut cukup dikopi dari file yang sudah didownload sebelumnya

Problem CPU AMD Ryzen 5600X

Baru saja menemukan masalah di Ubuntu desktop. Pada komputasi dengan beban ringan tidak ada masalah. Tapi kalau ada pengolahan data yang cukup berat terutama yang multi core, muncul pesan dari kernel dan komputer otomatis restart.

Deskripsi sistem:

Prosesor: AMD Ryzen 5 5600X
Mainboard: Asrock B450 Pro 4
Mainboard BIOS version: 4.80 (versi terbaru)
Memori: Corsair 32 GB (2×16)
Power Supply: Seasonic dan Corsair
Overclock: tidak ada overclock, setting di BIOS untuk CPU dan RAM menggunakan kecepatan standar (AUTO)
Sistem Operasi: Ubuntu 20.04

Percobaan di Ubuntu

Pengukuran #1 (via remote console)

Data berikut ini didapat dari remote console (SSH) dengan Putty. Pesan error tidak ada di /var/log, karena kernel panic tidak menghasilkan catatan log.

Kasus di core 5

kernel:[56582.292384] [Hardware Error]: Uncorrected, software restartable error.
kernel:[56582.292389] [Hardware Error]: CPU:5 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135
kernel:[56582.292394] [Hardware Error]: Error Addr: 0x0000000212bab300
kernel:[56582.292397] [Hardware Error]: IPID: 0x001000b000000000
kernel:[56582.292400] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load.
kernel:[56582.292405] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD

Kasus di core 5

kernel:[11836.000115] [Hardware Error]: Uncorrected, software restartable error.
kernel:[11836.000329] [Hardware Error]: CPU:5 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135
kernel:[11836.000539] [Hardware Error]: Error Addr: 0x00000002c338b300
kernel:[11836.000753] [Hardware Error]: IPID: 0x001000b000000000
kernel:[11836.000964] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load.
kernel:[11836.001178] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD

Kasus di core 10

kernel:[ 259.124195] [Hardware Error]: Uncorrected, software restartable error.
kernel:[ 259.124199] [Hardware Error]: CPU:10 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135
kernel:[ 259.124205] [Hardware Error]: Error Addr: 0x00000007a9b2bea0
kernel:[ 259.124207] [Hardware Error]: IPID: 0x001000b000000000
kernel:[ 259.124212] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load.
kernel:[ 259.124216] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD

Kasus di core 11

kernel:[29125.820062] [Hardware Error]: Uncorrected, software restartable error.
kernel:[29125.820259] [Hardware Error]: CPU:11 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135
kernel:[29125.820479] [Hardware Error]: Error Addr: 0x00000007ca1d9880
kernel:[29125.820681] [Hardware Error]: IPID: 0x001000b000000000
kernel:[29125.820892] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load.
kernel:[29125.821100] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD

Ryzen 5600X memiliki 6 core dengan 12 thread. Dari 12 itu, 3 bermasalah.

Pengukuran #2 (via serial console)

Tidak semua kejadian crash menghasilkan catatan pesan di console Putty. Untuk itu ditambahkan serial console supaya output dari console dapat direkam di komputer lain. Petunjuk menambahkan serial console di Ubuntu dirangkum ditulisan “Serial Console di Ubuntu 20.04“

Berikut ini hasil rekaman crash dengan serial console

Berikut teks rekaman kernel panic tersebut di detik 339

[ 339.133954] mce: [Hardware Error]: CPU 11: Machine Check Exception: 7 Bank 0: bc00080001010135
[ 339.142703] mce: [Hardware Error]: RIP 10:<ffffffffa58743eb> {csum_partial_copy_generic+0x4b/0x169}
[ 339.151887] mce: [Hardware Error]: TSC 13351e22605 ADDR 217f04700 MISC d01a000000000000 IPID 1000b000000000
[ 339.161907] mce: [Hardware Error]: PROCESSOR 2:a20f10 TIME 1621260956 SOCKET 0 APIC b microcode a201009
[ 339.171432] Kernel panic – not syncing: Fatal local machine check

Kasus berikut terjadi di detik ke 192

[ 192.279184] mce: [Hardware Error]: CPU 11: Machine Check Exception: 7 Bank 0: bc00080001010135
[ 192.288365] mce: [Hardware Error]: RIP 10:<ffffffff9ea7156e> {copy_user_enhanced_fast_string+0xe/0x30}

Kasus berikut terjadi di detik 25552

[25552.847056] mce: [Hardware Error]: CPU 11: Machine Check Exception: 7 Bank 0: bc00080001010135
[25552.856356] mce: [Hardware Error]: RIP 10:<ffffffff9487156e> {copy_user_enhanced_fast_string+0xe/0x30}
[25552.865975] mce: [Hardware Error]: TSC 55e329c132c5 ADDR 16fcb7680 MISC d01a000000000000 IPID 1000b000000000
[25552.876436] mce: [Hardware Error]: PROCESSOR 2:a20f10 TIME 1621288267 SOCKET 0 APIC b microcode a201009
[25552.886316] Kernel panic – not syncing: Fatal local machine check

Percobaan di Windows

Percobaan berikut ini dilakukan di Windows 10. Dilakukan pengujian beban komputasi yang banyak. Setelah beberapa jam, komputer restart sendiri. Pesan kesalahan dilihat di Event Viewer. Pesan error berikut ini menandakan ada masalah di core 11:

Percobaan diulangi lagi. Setelah beberapa jam, muncul pesan kesalahan. Pada kesempatan ini yang error adalah core 10:

Analisis

Dari hasil membaca berbagai artikel, kemungkinan ada cacat fisik di core nomor 5,10 dan 11
Referensi:

Linux Questions: Hardware Error
Forum Proxmox: Hardware Error’s reboots on it’s own, menurut artikel ini solusinya adalah RMA
BUgzilla Redhat: Possible Memory Corruption with AMD Zen 3 : belum ada solusi
Forums Unraid: Machine Check Error: faulty CPU

Solusi Sementara

Matikan core 5 , 10 dan 11

echo 0 > /sys/devices/system/cpu/cpu5/online

echo 0 > /sys/devices/system/cpu/cpu10/online

echo 0 > /sys/devices/system/cpu/cpu11/online

Cara mematikan core didapat di artikel [disabling cpu cores] [How To Disable CPU] dan [CPU hotplug in the Kernel]

Setelah 3 core tersebut dimatikan, maka permasalahan tidak muncul lagi. Hanya saja jadinya CPU 12 core menjadi tinggal CPU 9 core 🙂

Studi Literatur

Masalah serupa pernah terjadi di Ryzen seri 5000: “If You Buy an AMD Ryzen 5000 CPU, Make Sure You Keep the Box“

Solusi Permanen

Belum diketahui

Referensi

Mounting LVM di Ubuntu

vgscan
vgs
lvcreate -L 1000G -n data_volume2 ubuntu-vg
mkfs.xfs /dev/ubuntu-vg/data_volume2
mount /dev/ubuntu-vg/data_volume2 /data

Referensi

Mengaktifkan Nomor XL Hangus Secara Daring/Online

Sekarang dalam masa pandemi ini, XL menyediakan layanan mengaktifkan nomor yang hangus secara daring tanpa harus datang ke kantor XL.

Langkah-langkah resmi dari XL dapat dilihat disini.

Ini adalah pengalaman saya mengaktifkan nomor XL yang hangus.

Setelah membuka e-form dan memilih layanan reaktivasi kartu, pihak XL meminta data nomor yang ingin diaktifkan:

Tampilan website untuk mengaktifkan nomor.

Setelah itu, seperti yang sudah disebutkan pada petunjuk pada halaman resmi, e-form tadi meminta data diri, KTP dan Kartu Keluarga, sesuai dengan yang didaftarkan ketika nomor XL pertama kali diaktifkan. Dan jangan lupa nomor HP untuk video call. Pastikan nomor ini dapat melakukan video call karena pihak XL akan mengkonfirmasi pelanggan melalui video call, misalnya melalui whatsapp.

E-form tadi juga meminta 19 digit angka yang terletak dibelakang kartu XL yang ingin diaktifkan.

Selain itu, walaupun tidak tertera pada laman resmi, e-form juga akan meminta foto kartu XL tersebut, foto KTP dan juga foto diri beserta KTP.

Setelah semua data diunggah, dalam jangka waktu kalau tidak salah 48 jam, pihak XL akan menghubungi nomor yang dimasukkan untuk video call, dan akan segera melakukan video call jika pelanggan siap. Pihak XL akan meminta pelanggan menunjukkan KTP. Setelah itu, pihak XL akan mengaktifkan nomor dalam jangka waktu beberapa jam.

Setelah nomor aktif, pelanggan akan diminta mengisi pulsa, dan nomor XL akan siap digunakan kembali. Jika nomor masih tidak dapat digunakan, cobalah men-restart HP.

CommonLit Readability Prize: Resources

Competition Information

Competition Name: CommonLit Readability Prize
URL: https://www.kaggle.com/c/commonlitreadabilityprize/overview

Journals

Linguistic Features for Readability Assessment – Readability assessment aims to automatically classify text by the level appropriate for learning readers.
Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model
Learning to Predict Readability using Diverse Linguistic Features – In this paper we consider the problem of building a system to predict readability of natural-language documents.
A Machine Learning Approach to Measurement of Text Readability for EFL Learners Using Various Linguistic Features
Readability Assessment for Text Simplification – We describe a readability assessment approach to support the process of text simplification for poor literacy readers. Given an input text, the goal is to predict its readability level, which corresponds to the literacy level that is expected from the target reader: rudimentary, basic or advanced.
Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts – Text readability assessment is a challenging interdisciplinary endeavor with rich practical implications. It has long drawn the attention of researchers internationally, and the readability models since developed
have been widely applied to various fields. Previous readability models have only made use of linguistic features employed for general text analysis and have not been sufficiently accurate when used to gauge domain-specific texts.
Deep Learning for Prominence Detection in Children’s Read Speech – A previous well-tuned random forest ensemble predictor is replaced by an RNN sequence classifier to exploit potential context dependency across the longer utterance. Further, deep learning is applied to obtain word-level features from low-level acoustic contours of fundamental frequency, intensity and spectral shape in an end-to-end fashion. Performance comparisons are presented across the different feature types and across different feature learning architectures for prominent word prediction to draw insights wherever possible.
Crossley, S. A., Skalicky, S., & Dascalu, M. (2019). Moving beyond classic readability formulas: New methods and new models. Journal of Research in Reading, 42 (3-4), 541-561
Crossley, S. A., Skalicky, S., Dascalu, M., McNamara, D., & Kyle, K. (2017). Predicting text comprehension, processing, and familiarity in adult readers: New approaches to readability formulas. Discourse Processes, 54(5-6), 340-359.
Crossley, S. A., Greenfield, J., & McNamara, D. S. (2008). Assessing text readability using cognitively based indices. TESOL Quarterly, 42 (3), 475-493.
Crossley, S. A., Dufty, D. F., McCarthy, P. M., & McNamara, D. S. (2007). Toward a new readability: A mixed model approach. In D.S. McNamara and G. Trafton (Eds.), Proceedings of the 29th annual conference of the Cognitive Science Society (pp. 197-202). Austin, TX: Cognitive Science Society.
ReadNet: A Hierarchical Transformer Framework forWeb Article Readability Analysis [arxiv] [paperswithcode]
Hierarchical Transformer Network
Supervised and Unsupervised Neural Approaches to Text Readability [paperswithcode]
BERT
Linguistic Features for Readability Assessment [paperswithcode]
BERT + SVM
Text Readability Assessment for Second Language Learners [paperswithcode]
SVM

Related Competition

UMICH SI650 Text Readability Classification

Conventional Tools

Readability Python Library

External Data

Weebit Readability Benchmark Dataset

Plant Pathology 2021 – FGVC8 Competition: Resources

Official link: https://www.kaggle.com/c/plant-pathology-2021-fgvc8/overview

Dataset

Thapa, Ranjita; Zhang, Kai; Snavely, Noah; Belongie, Serge; Khan, Awais. The Plant Pathology Challenge 2020 data set to classify foliar disease of apples. Applications in Plant Sciences, 8 (9), 2020.

Previous Competition

Plant Pathology 2020 – Fine Grained Visual Categorization 7 “Identify the category of foliar diseases in apple trees”

Related Competition

Cassava Leaf Disease Classification, “Identify the type of disease present on a Cassava Leaf image”

Papers on Plant Image Detection

Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen, UP-DETR: Unsupervised Pre-training for Object Detection with Transformers – The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade-off multi-task learning of classification and localization in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask.
Strawberry Detection Using a Heterogeneous Multi-Processor Platform.
This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3\% over the test set comprised of 146 images.
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search – We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training.
Bag of Freebies for Training Object Detection Neural Networks [paperswithcode]
In this works, we explore training tweaks that apply to various models including Faster R-CNN and YOLOv3. These tweaks do not change the model architectures, therefore, the inference costs remain the same. Our empirical results demonstrate that, however, these freebies can improve up to 5% absolute precision compared to state-of-the-art baselines.
MMDetection: Open MMLab Detection Toolbox and Benchmark [github]
MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.
YOLOv4: Optimal Speed and Accuracy of Object Detection [github]
Cascade R-CNN: Delving into High Quality Object Detection, Conference on Computer Vision and Pattern Recognition, 2018
EfficientDet: Scalable and Efficient Object Detection [github]
Cascade Mask R-CNN)CBNet: A Novel Composite Backbone Network Architecture for Object Detection
Image Processing Based Detection of Fungal Diseases in Plants, ICICT 2014
Applications of Computer Vision in Plant Pathology: A Survey, Arch Computat Methods Eng 27, 611–632 (2020). https://doi.org/10.1007

Penyebab tembaga kabel otomatis air jadi hitam

Kabel tembaga dapat mengalami oksidasi, yaitu tembaga bereaksi dengan oksigen membentuk senyawa tembaga oksida.

Tembaga oksida memiliki warna hijau, namun lama kelamaan dapat menjadi hitam.

Referensi

Artikel “What Does It Mean When Copper Wire Turns Black?”

SSD NVMe 1TB

Berikut ini perbandingan parameter-parameter penting SSD NVMe dengan kapasitas 1 TB, dengan interface M.2.

Tipe-tipe yang dipilih pada tabel berikut ini adalah yang tersedia di pasaran pada bulan April 2021.

Data terutama diperoleh dari situs resmi pabrikan masing-masing SSD. Beberapa produk tidak menyertakan TBW (Terabytes Written) di situsnya, sehingga data TBW perlu dicari dari situs review terkait.

Pabrikan	Tipe	Harga	Read / Write	TBW	Harga/TBW	Garansi
V-Gen	SSD M.2 NVme 1TB – Hyper Series	2179000	3500 / 2500	640 [REF]	3404	3 tahun
Team	TM8FPE001T0C611	2227000	2100 / 1700	600 [REF]	3711	3 tahun
Team	TM8FP4001T0C101	2237000	3400 / 2900	1660	1347	3 tahun
Corsair	MP600 Core 1 TB M.2 NVMe PCIe Gen. 4 x4 SSD	2789000	4700 / 1950	225 [REF]	12395	5 tahun
Corsair	Force Series Gen.4 PCIe MP600 1TB NVMe M.2 SSD	4109000	4950 / 4250	1800 [REF]	2282
Kingston	A2000 M.2 PCIe NVMe 1TB	2128000	2200 / 2000	600	3546
WDC	Black SN850 SSD 1TB M.2 NVMe PCIe / SSD 1TB	4140000	7000 / 5300	600 [REF]	6900
WDC	Black SN750 NVMe SSD M.2 N SSD 1TB	3130000	3470 / 3000	600 [REF]	5216
WDC	Blue SN550 NVMe SSD 1TB M.2 PCIe	1893000	2400 / 1950	600 [REF]	3155
ADATA	SX6000 Lite 1TB	1694000	1800 / 1200	480 [REF]	3529	5 tahun
ADATA	SX6000 PRo 1TB	1729000	2100 / 1500	480 [REF]	3602	5 tahun
Transcend	TS1TMTE112S NVMe PCIe Gen3 x4 M.2 1TB / SSD 1TB	2010000	1700/1400	400	5025	5 tahun
Transcend	TS1TMTE110S NVMe PCIe Gen3 x4 M.2 1TB	2020000	1700 / 1400	400	5050	5 tahun

Teknik Pemasangan Sel Surya / Photovoltaic

Berikut ini beberapa teknik pemasangan sel surya:

Ground mounting photovoltaic: sel surya dipasang di permukaan tanah
Rooftop: sel surya dipasang di atap rumah
Floating phovotoltaic: sel surya diapungkan di permukaan air
BIPV (Building Integrated Photovoltaic): sel surya yang terintegrasi secara langsung dengan suatu bangunan
Agrivoltaic: Sel surya yang dipasang terintegrasi dengan pertanian.

Ground Mounting Photovoltaic

Sumber: http://www.apsolarracking.com/ground-solar-mounting.html

Rooftop Photovoltaic

Referensi https://en.wikipedia.org/wiki/Rooftop_photovoltaic_power_station

Floating Photovoltaic

Referensi

https://en.wikipedia.org/wiki/Floating_solar

BIPV (Building Integrated Photovoltaic)

Referensi: https://en.wikipedia.org/wiki/Building-integrated_photovoltaics

Agrivoltaic

Referensi: https://en.wikipedia.org/wiki/Agrivoltaic

Model Bisnis Semikonduktor

Berikut ini model-model bisnis perusahaan semikonduktor di dunia pada saat ini (2021):

IDM (Integrated Device Manufacturer)
Foundry
Fabless

Fabless

Fabless adalah perusahaan semikonduktor yang membuat dan menjual produk berbasis komponen semikonduktor, namun tidak melakukan fabrikasi sendiri komponen semikonduktornya. Komponen semikonduktor dibuat di pabrik lain (foundry).

Contoh perusahaan yang melakukan fabless:

Qualcomm
Nvidia
AMD
Mediatek

Integrated Device Manufacturer (IDM)

IDM (Integrated Device Manufacturer) adalah perusahaan semikonduktor yang melakukan desain (perancangan), manufakturing (fabrikasi) dan menjual produk rangkaian terintegrasi (IC/Integrated Circuit)

Contoh perusahaan yang melakukan IDM:

Foundry

Foundry adalah perusahaan semikonduktor yang melakukan fabrikasi produk semikonduktor saja.

Contoh perusahaan yang melakukan proses foundry:

Contoh perusahaan yang melakukan kombinasi model bisnis:

Samsung melakukan IDM dan foundry
Texas Instruments, Broadcomm, Infineon, STM melakukan IDM dan fabless

Pada saat ini Intel melakukan IDM, namun ke depannya ada rencana masuk ke foundry juga seperti Samsung

Referensi

Integrated Device Manufacturer
Foundry Model
Fabless Manufacturing
Intel https://www.intel.com
IBM https://www.ibm.com
Global Foundries https://www.globalfoundries.com/
SMIC http://www.smics.com/en
UMC (United Microelectronics Corporation https://en.wikipedia.org/wiki/United_Microelectronics_Corporation
TSMC https://www.tsmc.com/english
Kioxia https://www.kioxia.com/en-apac/top.html
SK Hynix https://www.skhynix.com/eng/index.jsp
Micron https://www.micron.com/
NXP https://www.nxp.com/
Broadcomm https://www.broadcom.com/
Infineon https://www.infineon.com/
STM https://www.st.com
EPROM chip https://commons.wikimedia.org/wiki/File:EPROM_Microchip_SuperMacro.jpg