sandeepkumar-skb

#include <stdio.h>
#include <thread>
#include <chrono>
#include <iostream>

const int N = 1 << 20;

__global__ void kernel(float *x, int n)
{
    int tid = threadIdx.x + blockIdx.x * blockDim.x;

sandeepkumar-skb

" Use Vim settings, rather then Vi settings (much better!).
" This must be first, because it changes other options as a side effect.
set nocompatible

" TODO: this may not be in the correct place. It is intended to allow overriding <Leader>.
" source ~/.vimrc.before if it exists.
if filereadable(expand("~/.vimrc.before"))
  source ~/.vimrc.before
endif

dojoteef

'''
Memory profiling utilities
'''
import gc
import inspect
import linecache
import os.path
import sys
import time
import threading

KellenSunderland

# -*- mode: dockerfile -*-
# Work in progress, some of the manual steps below will be fixed in a subsequent release.
# Dockerfile to build libmxnet.so, and a python wheel for the Jetson TX1 and TX2
# Builds from Github MXNet master branch
# Once complete copy artifacts from /work/build to target device.
# Install by running 'pip wheel name_of_wheel.whl' and copying the .so to a folder on your LD_LIBRARY_PATH

FROM nvidia/cuda:8.0-cudnn5-devel as cudabuilder

FROM dockcross/linux-arm64

Badel2

/* https://spectreattack.com/spectre.pdf */
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#ifdef _MSC_VER
#include <intrin.h> /* for rdtscp and clflush */
#pragma optimize("gt",on)
#else
#include <x86intrin.h> /* for rdtscp and clflush */
#endif

malcolmgreaves

import numpy as np
cimport numpy as np

cdef extern from "numpy/arrayobject.h":
    void PyArray_ENABLEFLAGS(np.ndarray arr, int flags)
    object PyArray_SimpleNewFromData(int nd, np.npy_intp* dims, int typenum, void* data)
    void _import_array()

cdef np.ndarray[np.int32_t, ndim=1, mode='c'] from_pointer_to_ndarray(np.npy_intp n_elements,
                                                                      np.int32_t* values):

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

danijar

# Example for my blog post at:
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

gkossakowski

Scala's "type T in class C as seen from a prefix type S" with examples

Introduction

Recently, I found myself in need to precisely understand Scala's core typechecking rules. I was particulary interested in understanding rules responsible for typechecking signatures of members defined in classes (and all types derived from them). Scala Language Specification (SLS) contains definition of the rules but lacks any examples. The definition of the rules uses mutual recursion and nested switch-like constructs that make it hard to follow. I've written down examples together with explanation how specific set of rules (grouped thematically) is applied. These notes helped me gain confidence that I fully understand Scala's core typechecking algorithm.

As Seen From

Let's quote the Scala spec for As Seen From (ASF) rules numbered for an easier reference:

Qwlouse