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update: this question is related to Google Colab's "Notebook settings: Hardware accelerator: GPU". This question was written before the "TPU" option was added. Reading multiple excited announcements about Google Colaboratory providing free Tesla K80 GPU, I tried to run fast.ai lesson on it for it to never complete - quickly running out of memory. I started investigating of why. The bottom line is that “free Tesla K80” is not "free" for all - for some only a small slice of it is "free". I connect to Google Colab from West Coast Canada and I get only 0.5GB of what supposed to be a 24GB GPU RAM.

int myInt; cout << myInt; // Garbage like 429948, etc If I output and/or work with uninitialized variables in C++, what are their assumed values? Actual values in the memory from the "last user"? e.g.: Program A is closed, it had an int with the value 1234 at 0x1234 -> I run my program, myInt gets the address 0x1234 , I output it like above -> 1234 Is it just random garbage? Alex Martelli "Random garbage" but with emphasis on "garbage", not on "random" – i.e., absolutely arbitrary garbage without even any guarantee of "randomness" – the compiler and runtime systems are allowed to have

FPGA面试题——网上资料整理 2019-08-23 21:22:30 1：什么是同步逻辑和异步逻辑？（汉王） 同步逻辑是时钟之间有固定的因果关系。 异步逻辑是各时钟之间没有固定的因果关系。 〔补充〕： 同步时序逻辑电路的特点：各触发器的时钟端全部连接在一起，并接在系统时钟端，只有当时钟脉冲到来时，电路的状态才能改变。改变后的状态将一直保持到下一个时钟脉冲的到来，此时无论外部输入 x 有无变化，状态表中的每个状态都是稳定的。 异步时序逻辑电路的特点：电路中除可以使用带时钟的触发器外，还可以使用不带时钟的触发器和延迟元件作为存储元件，电路中没有统一的时钟，电路状态的改变由外部输入的变化直接引起。 2：同步电路和异步电路的区别： 同步电路：存储电路中所有触发器的时钟输入端都接同一个时钟脉冲源，因而所有触发器的状态的变化都与所加的时钟脉冲信号同步。 异步电路：电路没有统一的时钟，有些触发器的时钟输入端与时钟脉冲源相连，这有这些触发器的状态变化与时钟脉冲同步，而其他的触发器的状态变化不与时钟脉冲同步。 3：时序设计的实质： 电路设计的难点在时序设计，时序设计的实质就是满足每一个触发器的建立/保持时间的要求。 4：建立时间与保持时间的概念？ 建立时间：触发器在时钟上升沿到来之前，其数据输入端的数据必须保持不变的时间。 保持时间：触发器在时钟上升沿到来之后，其数据输入端的数据必须保持不变的时间

Suppose I'm using AVX2's VGATHERDPS - this should load 8 single-precision floats using 8 DWORD indices. What happens when the data to be loaded exists in different cache-lines? Is the instruction implemented as a hardware loop which fetches cache-lines one by one? Or, can it issue a load to multiple cache-lines at once? I read a couple of papers which state the former (and that's the one which makes more sense to me), but I would like to know a bit more about this. Link to one paper: http://arxiv.org/pdf/1401.7494.pdf I did some benchmarking of the AVX gather instructions and it seems to be a

Is there a way to know the avaliable ram in a server (linux distro) with php (widthout using linux commands)? edit: sorry, the objective is to be aware of the ram available in the server / virtual machine, for the particular server (even if that memory is shared). rcoder If you know this code will only be running under Linux, you can use the special /proc/meminfo file to get information about the system's virtual memory subsystem. The file has a form like this: MemTotal: 255908 kB MemFree: 69936 kB Buffers: 15812 kB Cached: 115124 kB SwapCached: 0 kB Active: 92700 kB Inactive: 63792 kB ...

I have lot of dataframes created as part of preprocessing. Since I have limited 6GB ram, I want to delete all the unnecessary dataframes from RAM to avoid running out of memory when running GRIDSEARCHCV in scikit-learn. 1) Is there a function to list only, all the dataframes currently loaded in memory? I tried dir() but it gives lot of other object other than dataframes. 2) I created a list of dataframes to delete del_df=[Gender_dummies, capsule_trans, col, concat_df_list, coup_CAPSULE_dummies] & ran for i in del_df: del (i) But its not deleting the dataframes. But deleting dataframes

I'm typing a shell script to find out the total physical memory in some RHEL linux boxes. First of all I want to stress that I'm interested in the total physical memory recognized by kernel, not just the available memory . Therefore, please, avoid answers suggesting to read /proc/meminfo or to use the free , top or sar commands -- In all these cases, their " total memory " values mean " available memory " ones. The first thought was to read the boot kernel messages: Memory: 61861540k/63438844k available (2577k kernel code, 1042516k reserved, 1305k data, 212k init) But in some linux boxes, due