zsim | A fast and scalable x86-64 multicore simulator
kandi X-RAY | zsim Summary
kandi X-RAY | zsim Summary
zsim is a fast x86-64 simulator. It was originally written to evaluate ZCache (Sanchez and Kozyrakis, MICRO-44, Dec 2010), hence the name, but it has since outgrown its purpose. zsim’s main goals are to be fast, simple, and accurate, with a focus on simulating memory hierarchies and large, heterogeneous systems. It is parallel and uses DBT extensively, resulting in speeds of hundreds of millions of instructions/second in a modern multicore host. Unlike conventional simulators, zsim is organized to scale well (almost linearly) with simulated core count. You can find more details about zsim in our ISCA 2013 paper:
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QUESTION
I'm trying to create an accurate and efficient searching algorithm for the system. I installed Postgresql to utilize its trigram similarity query, and this is how i searched for objects:
...ANSWER
Answered 2018-Feb-12 at 14:31The confusion you're facing is caused by Django's deferred evaluation of QuerySets. The 0.5s you're waiting for Django is actually only preparing the SQL - that is to say, converting the ORM calls into a SQL query, (or several SQL queries), that it can later execute.
QuerySets are evaluated as late as possible then cached, so to find out how long it's actually taking through Django, you'll need to force evaluation of the QuerySet, to get Django to execute the SQL. You can do this in a couple of ways, such as:
QUESTION
Having read the following questions:
using one random engine for multi distributions in c++11
Uncorrelated parallel random seeds with C++ 2011?
std::default_random_engine generate values between 0.0 and 1.0
how to generate uncorrelated random sequences using c++
Using same random number generator across multiple functions
and having experienced a few tricks has rised doubts in my conceptual understanding on random generators for multiple (different) distributions in c++. In particular:
- Is it OK to use one generator for drawing numbers in different distributions (uniform, binomial, ...) as long as you don't multithread?
For instance, assume i'm using the following:
...ANSWER
Answered 2018-Jan-04 at 11:44If you read about UniformRandomBitGenerator you will find that the random generator will generate random bits which ideally are pretty much independent from one another, to the extent the PRNG in question can achieve this. So essentially every call to engine() will generate one almost uncorrelated integer. It's the task of the distribution to make the appropriate number of calls to this. A single bit distribution might make a single call to a 32bit engine for every 32 calls to the distribution itself, caching unused entropy between calls. Conversely a double precision number generator might use entropy from two 32bit engine results to determine all 53 mantissa bits of a double. The engine doesn't care which distribution consumes its random bits, so using the same engine in different distributions isn't a problem.
If you read https://en.wikipedia.org/wiki/Mersenne_Twister you will find that it is
k-distributed to 32-bit accuracy for every 1 ≤ k ≤ 623 (for a definition of k-distributed, see below)
So if you use std::mt19937 I'd say you should be safe to use the same engine in up to 623 different distributions, no matter whether they are of the same or different types. For more distributions it depends on how they are to be used, but in most cases I wouldn't worry too much either.
QUESTION
I want to compile zsim (https://github.com/s5z/zsim)
...ANSWER
Answered 2017-Jul-05 at 23:24A quick look at the readme specifies the following: (https://github.com/s5z/zsim/blob/master/README.md)
Download Pin, http://www.pintool.org . Tested with Pin 2.8+ on an x86-64 architecture. Compiler flags are set up for Pin 2.9 on x86-64. To get flags for other versions, examine the Pin makefile or derive from sample pintools. Set the PINPATH environment variable to Pin's base directory.
Sounds like you need to do the following
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Install zsim
Clone a fresh copy of the git zsim repository (git clone <path to zsim repo>).
Download Pin, http://www.pintool.org . Tested with Pin 2.8+ on an x86-64 architecture. Compiler flags are set up for Pin 2.9 on x86-64. To get flags for other versions, examine the Pin makefile or derive from sample pintools. Set the PINPATH environment variable to Pin’s base directory. NOTE: Linux 3.0+ systems require Pin 2.10+, just because Pin does a kernel version check that 3.0 fails. Use Pin 2.12 with Sandy/Ivy Bridge systems, earlier Pin versions have strange performance regressions on this machine (extremely low IPC).
zsim requires some additional libraries. If they are not installed in your system, you will need to download and build them: 3.1 libconfig, http://www.hyperrealm.com/libconfig. You may use the system's package if it's recent enough, or build your own. To install locally, untar, run `./configure --prefix=<libconfig install path> && make install`. Then define the env var `LIBCONFIGPATH=<libconfig install path>`. 3.2 libhdf5, http://www.hdfgroup.org (v1.8.4 path 1 or higher), and libelfg0. The SConstruct file assumes these are installed in the system. 3.3 (OPTIONAL) polarssl (currently used just for their SHA-1 hash function), http://www.polarssl.org Install locally as in 3.1 and define the env var `POLARSSLPATH=<polarssl install path>`. NOTE: You may need to add `-fPIC` to the Makefile's C(PP/XX)FLAGS depending on the version. 3.4 (OPTIONAL) DRAMSim2 for main memory simulation. Build locally and define the env var DRAMSIMPATH as in 3.1 and 3.3.
In some distributions you may need to make minor changes to the host configuration to support large shmem segments and ptrace. See the notes below for more details.
Compile zsim: scons -j16
Launch a test run: ./build/opt/zsim tests/simple.cfg
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