Singkat cerita saya sudah memiliki modem tersebut. Namun anehnya begini teman, kalau saya ingin koneksi di Ubuntu 10.04 saya, saya harus terlebih dulu login ke Windows, connect modem tersebut, disconnect dan reboot ke Ubuntu 10.04 saya. (Laptop saya dualboot, meskipun Windowsnya sangat-sangat jarang sekali saya gunakan, kecuali game ). Jika saya mengkoneksikan langsung di Ubuntu, modem tidak akan terdetek. Singkat cerita saya googling sana-sini dan menemukan langkah-langkah berikut ini :

PS : Saya menemukannya menggunakan cara ini, jikalau teman-teman mempunyai versi sendiri silakan di share yah. Kebetulan sebelumnya sudah terinstall usb-modeswitch dan usb-modeswitch-data, namun EC1261-2 tetap tidak terbaca, karena kemungkinan harus menggunakan Versi usb-modeswitch terbaru.

1. Uninstall terlebih dahulu usb-modeswitch dan usb-modeswitch-data yang sudah terinstall

2. Install libusb-dev

3. Download 2 paket dibawah ini :
usb-modeswitch-1.1.9
usb-modeswitch-data

Note, info dari salah satu pembaca saya bahwa usb-modeswitch data sudah tidak ada lagi, sepertinya sudah di hapus oleh author webnya dan di ganti versi yang paling baru, di bawah ini link download versi usb-modeswich dan usb-modeswitch-data yang terbaru :

usb-modeswitch-1.2.0
usb-modeswitch-data update tanggal 23 Oktober 2011

4. Extract terlebih dahulu usb-modeswitch-data

5. Extract usb-modeswitch-1.1.9

6. Setelah selesai. Silakan reboot komputer anda. Insya Allah setelah nanti di colokkan modem Smartfren Huawei EC1261-2 otomatis terdeteksi

Selamat Mencoba

PS : tambahan bagi anda yang ingin mendownload interface koneksi mobile partenr di linux silakan menuju link berikut :

http://www.multiupload.com/W6CWMMRZE5

Update :

Untuk anda yang sudah men-Download Mobile Partner, untuk menginstallnya silakan :

1. Unzip File mobile partner tsb
2. Masuk ke dalam folder Mobile PArtner hasil extract an tadi
3. tambahkan atribut +x agar bisa di eksekusi di file install:

(dalam mode root)

4. Setelah itu silakan di eksekusi

5. Nanti silakan tekan enter saja untuk folder instalasi dan saat instalasi JRE
6. Mobile Partner otomatis akan ada di => Applications -> Internet -> Mobile Partner

Cara 1

1.Buat folder baru di /media/ (kali ini nama folder nya adalah windows) :

2. Edit file /etc/fstab (Saya menggunakan editor nano, anda bisa menggunakan gedit, vim, atau editor lainnya):

2. Tambahkan baris berikut ini di taruh di bagian yang paling bawah saja :

/dev/sda1 /media/windows ntfs-3g rw,nosuid,nodev,shortname=mixed,uid=1000,utf8,umask=077,flush 0 0

Ada yang me rekomendasikan untuk menulis ini :
/dev/sda1 /media/windows vfat auto,users,uid=1000,gid=1000,utf8,dmask=027,fmask=137 0 0

Isi dari fstab yang di atas ini saya belum coba, apakah bisa atau tidak, silakan di tes saja
Notes :
/dev/sda1 = partisi ntfs/Windows anda. Unuk mengetahui partis ntfs anda, dapat menggunakan perintah di abwah ini melalui terminal :

Output dari perintah di atas akan menunjukkan parisi drive NTFS anda, cek di bagian paling ujung kanan, yang menandakan tipe filesystem anda adalah NTFS.

/media/windows = folder yang anda buat pertama kali di dalam folder /media/ . Folder /media/windows/ ini untuk menaruh hasil mounting dari /dev/sda1 tadi.

Cara 2

Menggunakan aplikasi pysdm

Instalasi :

1. Bisa melalui terminal dengan perintah :

Atau melalui synaptic atau software center, seach saja pysdm (pastikan anda telah mengkonfigurasi alamat download repositori software dengan baik)

2. Menjalankan pysdm :

System –> Administration –> Storage Device Manager

pysdm 1

Di Tab General Configuration anda akan melihat name dan mounpoint, name nya bisa anda ganti dan mount point nya juga, secara default akan di simpan di dalam /media. Di sebelah kiri anda memastikan letak posisi partisi drive nya ada dimana, misal sda1 –> mount pointnya ke /media/driveC, maka di kolom kiri anda klik sda1, dan d tab general informationnya anda memilih lokasi mount pointnya.

Selesai. Mudah bukan?

Selamat Mencoba

1. Siapkan Kopi dan Cemilan, biar ga suntuk

2. Unduh Driver Canon IP1980 di Situs Resmi Canon. Silakan merujuk ke link ini untuk mendownload drivernya. Pilih Linux untuk OS nya dan english bahasanya.

3. Hasil download file tersebut akan terdapat sebuah paket tar yang berisi 2 buah file, yang satu berupa deb dan satu buah paket tar.gz. Ekstrak paket tersebut, kemudian install semua file deb yang ada di folder hasil ekstrakan.

4. Sebelum di Install, Install Dependensi libcupsys2 terlebih dahulu. bagi pengguna jaunty silakan tambahkan baris berikut di dalam /etc/apt/sources.list

deb http://security.ubuntu.com/ubuntu jaunty-security main universe

Bagi yang pake karmic tinggal ganti aja jadi karmic 9.10

5. Setelah itu install libcupsys2 :
$sudo apt-get install libcupsys2

6. Setelah itu kembali ke folder hasil extractan Download file dari Canon tadi, yang ada file deb nya. Install deb nya dengan cara :

$sudo dpkg -i *.deb

7. Selesai. Tinggal Idupin printer, colok ke komputer

Selamat Mencoba

Graphics processing units (GPU) are evolving to provide a diverse range of image processing functions flexibly and at high speed; as a result they are morphing into architectures appropriate for general-purpose calculating engines. The term “GPU computing” has been applied to the idea of utilizing their capabilities for high-speed processing of applications such as medical imagery processing. We spoke to David Kirk (right), chief scientist at NVIDIA Corp, a manufacturer of GPUs. Kirk is a leading proponent of GPU computing, and the person behind NVIDIA’s development thrust.
You’ve been heavily promoting GPU computing recently.

What made you decide it was such an important development?

Parallel computing is entering a very interesting stage right now. It used to be that parallel computing environments were only available on supercomputers costing hundreds of millions of dollars, but today anyone can buy a system.

I have always been involved in computer graphics technology, such as GPU development for personal computers (PC). Now that GPUs are programmable we’re beginning to see adoption of parallel computing by general computer users. GPUs can make a real contribution to spreading parallel computing technology into new fields such as education and science. GPUs are going to be crucial tools not only in displaying imaging, but in all types of computation.
There are not many successful examples of using multiple processor cores to run software in parallel, are there?

I think it’s a bit premature to categorize all those attempts as failures. The knowledge, design approaches and other tips gained are being carried on in parallel computing architectures today. There is one element that has not ever succeeded, which is educating software developers in parallel computing.

For the past 20 or 30 years, software developers haven’t needed to consider parallel computing at all, because the advantages gained from higher microprocessor operating frequencies were just there for the taking. Now that microprocessor manufacturers are switching over to multicore designs, however, parallel computing has become a basic assumption in software design. Everyone has to take it into account now.

The actual coding needed to enable parallel processing is simple. In most cases, though, that alone doesn’t provide any improvement in execution performance as the number of cores is increased. The performance improvement gained by automatically converting a sequential program intended for a single-processor system to run under parallel processing is not very impressive, either.

The key is the stage before actual program coding: analyzing the problem and visualizing the parallel task structure needed to process it. This approach to parallel processing – I refer to it as “computational thinking” – is something that software developers really need to master.

For a developer, this should be something fun, like solving a puzzle. When educating programmers it shouldn’t be the first lesson, of course, but I do think that computational thinking dealing with parallel processing should be taught in the second. Parallel computing should be introduced into all kinds of equipment, from supercomputers to mobile phones. We want programmers to utilize Compute Unified Device Architecture (CUDA) as the development environment.

What are the critical architectural differences between GPUs and microprocessors?

Microprocessors have supported extended instruction sets for single instruction, multiple data (SIMD) processing for some time, and recently it has become common to mount multiple central processing unit (CPU) cores.
Microprocessors are designed to execute a given sequence of instruction groups in the shortest time possible. This basic approach is the same even if the chip has multiple CPU cores.

To ensure that the CPU cores can keep on executing instructions without entering the idle state, the time spent waiting for the data has to be very short. Even in CPU cores with an out-of-order execution function, allowing instructions with data read operations to be executed first, there are usually only two computation pipelines, and the data wait time is still a critical factor. That’s why a massive cache is used. In terms of area, caches can account for 80% or more of modern microprocessors.

Caches are very different in GPUs. GPUs are designed for applications with high degrees of parallelism. In graphics processing, for example, a GPU might be used to calculate position, process shading and perform other operations on millions of triangles, for all the colors of millions of pixels. That would mean millions of simultaneous tasks. They have the threads needed to handle multiple data, and are designed from the start for parallel processing applications, so the data does not need to be cached in advance.

When a particular thread requests data, it sleeps. While the data is being read from the camcorder, a different thread that requested data at an earlier point in time and has been sleeping is executed on a different processor core. The architecture executes data read and processing pipeline-style, so much less of the chip footprint is taken up by memory. Unlike microprocessors, most of the chip area is processor core. The GPU’s processing efficiency per unit area is very high.

Existing GPUs combine the characteristics of two architectures: SIMD and multiple instruction, multiple data (MIMD). Standard SIMD operation units cannot control branching, random addressing and similar things independently, but the GPU processor core has a great degree of freedom in controlling branching and such depending on the data. Because a single program, as opposed to a single instruction, can be executed for multiple data, I refer to this as single program, multiple data (SPMD) architecture.

What are your thoughts on the Larrabee graphics IC plan announced by Intel Corp, or on the idea of AMD Inc to combine CPU and GPU cores at the instruction set architecture level?