arpan kapoor

sqlite caching with vmtouch

2022-11-26T00:00:00+00:00

I have a script that queries a sqlite database file with ~8M rows in the main table and measures ~1.3GiB on disk. With a cold start, the script takes around 6 minutes to execute:

$ # clear the cache: https://www.kernel.org/doc/Documentation/sysctl/vm.txt
$ sudo sync
$ echo 3 | sudo tee /proc/sys/vm/drop_caches

$ time ./target/release/app
...
...

real    6m7.636s
user    0m14.322s
sys     0m15.043s

From time’s output, we can infer that the script is spending most of its time on I/O.

Since the db file is not large, bringing the entire file into the disk/page cache should speed things up. To do this, I found a small utility called vmtouch. The code it uses to preload is straightforward: memory map the file and then read it with a stride of PAGESIZE.

The total runtime (including time to cache the file) reduces to ~24 seconds giving a speed up of ~15x:

$ # clear the cache
$ sudo sync
$ echo 3 | sudo tee /proc/sys/vm/drop_caches

$ vmtouch -vt db.sqlite
[OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO] 319313/319313

           Files: 1
     Directories: 0
   Touched Pages: 319313 (1G)
         Elapsed: 9.6319 seconds

real    0m9.674s
user    0m0.084s
sys     0m0.569s
$ time ./target/release/app
...
...

real    0m14.051s
user    0m7.056s
sys     0m6.796s

exploring GPU programming with pycuda & VGG16

2022-05-29T00:00:00+00:00

In this post, I try to implement an inference module for a simple CNN model to help me get familiarized with GPU programming concepts.

history

Graphics Processing Units, as the name suggests were originally designed to render 2D and 3D graphics. For this, they exposed APIs such as OpenGL/Direct3D that were aimed at doing pixel/graphic math. See this for examples.

architecture

Compared to a CPU, a GPU consists of many more cores (NVIDIA 3080 Ti has 10240 cores), but each of them is slower than a typical CPU core (3080 runs at 1.37 GHz).

This makes them suitable for data parallel tasks where you want to execute the same operation on large amounts of data in parallel (SIMT instead of SIMD). In contrast, CPUs excel at fast sequential execution of instructions, which is suitable for applications that need low latency rather than high throughput.

The below diagram depicts the hardware architecture of an older NVIDIA GPU:

the GPU has a separate DRAM from the CPU’s DRAM
the GPU is divided into Streaming Multiprocessors (3080 has 80 SMs)
each SM contains a number of cores that perform the actual computation (each SM in 3080 has 128 cores)

NVIDIA Fermi micro-architecture (src)

CUDA

Scientists/tinkerers started using GPUs for non-graphics problems that fit the SIMT paradigm, such as linear algebra (eventually ML/deep learning). A major issue with this was that they had to shoehorn their solutions to the graphics-oriented APIs exposed by GPUs.

In ~2006, NVIDIA comes up with CUDA, opening the doors to general purpose computing on GPUs.

CUDA mainly provides the ability to program the GPU using a set of extensions to C/C++, which are compiled down to PTX (NVIDIA GPU’s instruction set) using their proprietary compiler (nvcc).

It exposes the GPU hardware to the programmer as a grid of threads. Each grid is further divided into independently scheduled blocks of threads, each of which executes on a single SM.

The number of thread blocks executing a kernel function (a C/C++ function that executes on the GPU) can be configured using the <<<NoOfBlocks, NoOfThreads>>> syntax.

GPU grids (original src)

Note: CUDA is only available on NVIDIA graphics cards. OpenCL is the vendor independent equivalent, although CUDA has a larger amount of support/documentation/libraries.

simple example

// compile using `nvcc example.cu`
#include <cuda.h>
#include <stdio.h>

__global__ void vecAddKernel(float *A, float *B, float *C, int n) {
  int i = blockDim.x * blockIdx.x + threadIdx.x;
  if (i < n)
    C[i] = A[i] + B[i];
}

void vecAdd(float *h_A, float *h_B, float *h_C, int n) {
  int size = n * sizeof(float);
  float *d_A, *d_B, *d_C;

  cudaMalloc((void **)&d_A, size);
  cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
  cudaMalloc((void **)&d_B, size);
  cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
  cudaMalloc((void **)&d_C, size);

  vecAddKernel<<<ceil(n / 256.0), 256>>>(d_A, d_B, d_C, n);

  cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);

  cudaFree(d_A);
  cudaFree(d_B);
  cudaFree(d_C);
}

int main() {
  float a[] = {1, 2, 3, 4};
  float b[] = {1, 2, 3, 4};
  float c[] = {0, 0, 0, 0};

  vecAdd(a, b, c, 4);
  for (int i = 0; i < 4; i++) {
    printf("%f ", c[i]);
  }
  printf("\n");
}

example from Programming Massively Parallel Processors

vecAdd above is a normal C/C++ function that:

allocates memory on the GPU (cudaMalloc)
copies the required data from the CPU DRAM (called host memory in CUDA terminology) to the GPU DRAM (device memory) using cudaMemcpy
calls the kernel function with a set number of thread blocks of equal size
copies the computed results back to the CPU DRAM (cudaMemcpy)
frees GPU DRAM (cudaFree)

vecAddKernel is a kernel function (indicated by __global__) that adds vectors A, B and stores them in C. This kernel gets executed by ceil(n / 256.0) blocks, each consisting of 256 threads. Since each thread is called with the same arguments, it uses some of the CUDA built-in variables (that provide the position of the thread in its grid) to choose the index that it should operate on. Finally, the if condition prevents out of bounds access in case the data size is not evenly divisible by the threads in each block (blockDim).

VGG16

VGGNet is a simple CNN (Computational Neural Network) architecture, put forward by the Visual Geometry Group at Oxford in 2014. The version with 16 weight layers (13 convolution + 3 fully connected layers), called VGG16 is available pretrained (on ImageNet) from Tensorflow.

I first wrote a simple python version of the VGG16 inference to make sure that my output matches with that of tensorflow.

import numpy as np
import tensorflow as tf
from PIL import Image

# from https://github.com/keras-team/keras/blob/07e13740fd181fc3ddec7d9a594d8a08666645f6/keras/applications/imagenet_utils.py#L168-L238
def preprocess_img(img):
    x = img.astype(np.float32)
    # 'RGB'->'BGR' (because of opencv?)
    x = x[..., ::-1]
    mean = [103.939, 116.779, 123.68]
    x[..., 0] -= mean[0]
    x[..., 1] -= mean[1]
    x[..., 2] -= mean[2]
    return x

# read sample image using PIL and resize to the size required by VGG16
img = np.asarray(Image.open('images/n02504458_12101.jpeg').resize((224, 224)))

# preprocess input image as done by tensorflow
img = preprocess_img(img)

# create tensorflow model
model = tf.keras.applications.vgg16.VGG16()

# get class probabilities
tfoutput = model.predict(img[np.newaxis, ...])[0]

To get the class labels corresponding to the highest probability outputs from the model, I saved the class number to label mapping for ImageNet into a dictionary called IMAGENET_LABELS in labels.py and imported it.

# from https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a#file-imagenet1000_clsidx_to_labels-txt
from labels import IMAGENET_LABELS

# return top predictions with probabilities from output of last layer
def get_top_predictions(preds, top=5):
    return {IMAGENET_LABELS[x]: preds[x] for x in (-preds).argsort()[:top]}

>>> get_top_predictions(tfoutput)
{'fur coat': 0.98191327,
 'Labrador retriever': 0.006128772,
 'Eskimo dog, husky': 0.0019237188,
 'golden retriever': 0.0015210236,
 'pug, pug-dog': 0.0012324217}

python implementation

The first step for the numpy implementation is to get the pretrained weights. The tensorflow model weights are available as a hdf5 file, which is a dictionary-like object with the layer names as the key and the weights and biases as the value for that layer.

import h5py
# from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5
VGG_WTS = h5py.File("vgg16_weights_tf_dim_ordering_tf_kernels.h5", "r")

# layer names from https://github.com/keras-team/keras/blob/v2.9.0/keras/applications/vgg16.py#L43-L227
# can also be obtained by executing `model.summary()` on the `Model` object
VGG_LAYERS = ['block1_conv1', 'block1_conv2', 'block1_pool',
              'block2_conv1', 'block2_conv2', 'block2_pool',
              'block3_conv1', 'block3_conv2', 'block3_conv3', 'block3_pool',
              'block4_conv1', 'block4_conv2', 'block4_conv3', 'block4_pool',
              'block5_conv1', 'block5_conv2', 'block5_conv3', 'block5_pool',
              'flatten',
              'fc1', 'fc2',
              'predictions']

Next, we write the activation functions: ReLU for the hidden layers and softmax for the output layer:

def relu(x):
    return np.maximum(x, 0)

def softmax(x):
    exp = np.exp(x)
    return exp / np.sum(exp)

Now, we can proceed to implement the 2D convolution operation. The inputs required for this will be the layer weights, biases and the output from the previous layer. Before proceeding, lets check the shapes of these inputs for the first layer.

# for the first layer, input is the image
>>> img.shape
(224, 224, 3)

# weights and biases for first CONV layer
>>> w, b = VGG_WTS[VGG_LAYERS[0]].values()

# the first layer of VGG16 is:
# x = layers.Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')
# 64 kernels of size 3x3 with padding to make sure output has same size as input

# 64 3x3 kernels (1 per input channel: corresponds to the 3rd 3 below)
>>> w.shape
(3, 3, 3, 64)

# 1 bias per kernel
>>> b.shape
(64,)

Convolution with 1 kernel (src)

In the above image, the blue cuboid corresponds to an output from one of the layers (feature map), which is being convolved with a single kernel (orange cuboid) to form 1 channel of the output.

The implementation follows:

def applyConv2d(w, b, inp):
    """assuming odd-sized square kernel"""
    ksize = w.shape[0]
    out_channels = w.shape[-1]

    # pad the input, not output: https://stackoverflow.com/a/69544897
    padded_inp = np.pad(inp, ((ksize//2, ksize//2),
                              (ksize//2, ksize//2),
                              (0, 0)))

    # output is same shape as input with more channels
    out = np.zeros(inp.shape[:2] + (out_channels,), dtype=np.float32)

    # convolve the surrounding of each pixel (3x3x3) with the kernel
    for x in range(inp.shape[0]):
        for y in range(inp.shape[1]):
            for c in range(out_channels):
                out[x][y][c] = np.tensordot(w[..., c],
                                            padded_inp[x:x+ksize, y:y+ksize],
                                            ksize)

    # add bias to each output channel
    for c in range(out_channels):
        out[..., c] += b[c]

    # apply relu activation
    return relu(out)

Each convolutional block of VGG16 ends with a max pooling layer, which is used to reduce the dimensions of the intermediate feature maps. The VGG16 max pooling is quite simple to implement since it uses a 2x2 kernel with a stride of 2, which reduces both the height and width of the feature map to half its original value. For instance, in the first block, max pooling reduces the feature map from (224, 224, 64) to (112, 112, 64).

def applyMaxPool2d(inp):
    """2x2 kernel with (2,2) stride"""
    out = np.zeros((inp.shape[0]//2, inp.shape[1]//2, inp.shape[2]), dtype=np.float32)
    for x in range(out.shape[0]):
        for y in range(out.shape[1]):
            for c in range(inp.shape[2]):
                out[x][y][c] = np.max(inp[2*x:2*(x+1), 2*y:2*(y+1), c])
    return out

Lastly, we write a function to apply all layers of VGG16 to the input image:

def applyVgg16(inp):
    curr = inp
    outputs = []
    for layer in VGG_LAYERS:
        if "pool" in layer:
            out = applyMaxPool2d(curr)
        elif layer == "flatten":
            out = curr.flatten()
        # weight layers
        else:
            w, b = (np.array(x) for x in VGG_WTS[layer].values())
            if layer.startswith("fc") or layer == "predictions":
                # fully connected layers are a simple matrix multiplication
                out = np.matmul(w.T, curr.reshape(-1, 1)).flatten() + b
                out = relu(out) if layer.startswith("fc") else softmax(out)
            else:
                out = applyConv2d(w, b, curr)
        outputs.append(out)
        print(f"processed {layer}: inshape: {curr.shape}, outshape: {out.shape}")
        curr = out

    # return output of all hidden layers along with output layer
    # helps in inspecting output of hidden layers
    return outputs

Now we call the above function on the preprocessed image and print the predictions:

start = datetime.datetime.now()
outputs = applyVgg16(img)
end = datetime.datetime.now()

print("predictions: ", get_top_predictions(outputs[-1]))
print(f"in {end-start}")

$ ./numpy_vgg16.py
processed block1_conv1: inshape: (224, 224, 3), outshape: (224, 224, 64)
processed block1_conv2: inshape: (224, 224, 64), outshape: (224, 224, 64)
processed block1_pool: inshape: (224, 224, 64), outshape: (112, 112, 64)
processed block2_conv1: inshape: (112, 112, 64), outshape: (112, 112, 128)
processed block2_conv2: inshape: (112, 112, 128), outshape: (112, 112, 128)
processed block2_pool: inshape: (112, 112, 128), outshape: (56, 56, 128)
processed block3_conv1: inshape: (56, 56, 128), outshape: (56, 56, 256)
processed block3_conv2: inshape: (56, 56, 256), outshape: (56, 56, 256)
processed block3_conv3: inshape: (56, 56, 256), outshape: (56, 56, 256)
processed block3_pool: inshape: (56, 56, 256), outshape: (28, 28, 256)
processed block4_conv1: inshape: (28, 28, 256), outshape: (28, 28, 512)
processed block4_conv2: inshape: (28, 28, 512), outshape: (28, 28, 512)
processed block4_conv3: inshape: (28, 28, 512), outshape: (28, 28, 512)
processed block4_pool: inshape: (28, 28, 512), outshape: (14, 14, 512)
processed block5_conv1: inshape: (14, 14, 512), outshape: (14, 14, 512)
processed block5_conv2: inshape: (14, 14, 512), outshape: (14, 14, 512)
processed block5_conv3: inshape: (14, 14, 512), outshape: (14, 14, 512)
processed block5_pool: inshape: (14, 14, 512), outshape: (7, 7, 512)
processed flatten: inshape: (7, 7, 512), outshape: (25088,)
processed fc1: inshape: (25088,), outshape: (4096,)
processed fc2: inshape: (4096,), outshape: (4096,)
processed predictions: inshape: (4096,), outshape: (1000,)
predictions:  {'African elephant, Loxodonta africana': 0.9346673, 'tusker': 0.06419284, 'Indian elephant, Elephas maximus': 0.0011378175, 'warthog': 6.19383e-07, 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis': 3.1221526e-07}
computed in 0:02:46.589266

So, our pure numpy VGG16 inference implementation takes around 3 minutes to process 1 image.

Now lets see how much this could be sped up with a naive CUDA implementation of the slow operations (convolution).

speed up using CUDA

The first task to use CUDA is to divide the problem into (gridDimension, blockDimension) sized sub-problems.

Since the number of threads in a block is limited to 1024 and the number of output channels of any layer in VGG16 does not exceed 512, we can set the blockDim to the output channel count for each layer.

The gridDim can be set to the image/feature map (height, width).

import pycuda.gpuarray
import pycuda.driver as cuda
import pycuda.autoinit

def applyConv2dCuda(w, b, inp):
    ksize = w.shape[0]
    inp_channels = inp.shape[-1]
    out_channels = w.shape[-1]
    padded_inp = np.pad(inp, ((ksize//2, ksize//2),
                              (ksize//2, ksize//2),
                              (0, 0)))
    out = np.zeros(inp.shape[:2] + (out_channels,), dtype=np.float32)

    # convert numpy arrays to pycuda.gpuarray
    in_gpu, out_gpu = pycuda.gpuarray.to_gpu(padded_inp), pycuda.gpuarray.to_gpu(out)
    w_gpu, b_gpu = pycuda.gpuarray.to_gpu(w), pycuda.gpuarray.to_gpu(b)

    # call cuda implementation with the GPU arrays
    cudaConv2d(in_gpu, out_gpu, w_gpu, b_gpu, np.int32(inp_channels), np.int32(ksize),
               block=(out_channels, 1, 1), grid=inp.shape[:2])

    # copy back the output from GPU memory
    return out_gpu.get()

The setup code to call the cuda function cudaConv2d is similar to the example we looked at earlier. In this case, pycuda provides us an easy way to copy numpy ndarray-like data from the CPU to the GPU using its GPUArray class.

Now, we write our CUDA code for convolution. The below kernel calculates the value of 1 pixel in the output feature map of a layer.

The most convoluted part here is the calculation of the indices into the various input arrays, since all of them are multi-dimensional, but represented by a single-dimensional array in C++.

I encountered an error cuMemFree failed: an illegal memory access was encountered, which was likely due to out of bounds access into the input array. Fortunately, NVIDIA kernels support printf, which made debugging these issues easier.

__global__ void conv2d(const float *inp, float *out, const float *w,
                       const float *b, int inp_channels, int ksize) {
  int channel_num = threadIdx.x;
  int out_channels = blockDim.x;

  // pixel (x, y)
  int x = blockIdx.x;
  int y = blockIdx.y;

  int outidx = out_channels * gridDim.y * x +
               out_channels * y +
               channel_num;

  for (int i = 0; i < ksize; i++) {
    for (int j = 0; j < ksize; j++) {
      for (int k = 0; k < inp_channels; k++) {

        // w is 4D with dimensions: (ksize, ksize, input_channels, output_channels)
        int widx = (ksize * inp_channels * out_channels * i) +
                   (inp_channels * out_channels * j) +
                   (out_channels * k) +
                   channel_num;

        // inp is 3D with dimensions: (blockDim.x + padding, blockDim.y + padding, input_channels)
        int inpidx = ((gridDim.y + ksize - 1) * inp_channels * (i + x)) +
                     (inp_channels * (j + y)) +
                     k;

        out[outidx] += inp[inpidx] * w[widx];
      }
    }
  }

  // add bias
  out[outidx] += b[channel_num];

  // relu
  if (out[outidx] < 0) {
    out[outidx] = 0.0;
  }
}

To be able to compile and run this CUDA code at runtime, we provide the above code as a python string to pycuda’s SourceModule class

from pycuda.compiler import SourceModule

src = """...placeholder for CUDA code..."""
mod = SourceModule(src)
cudaConv2d = mod.get_function("conv2d")

Finally, after executing this, we obtain the following output which is the same as the one from tensorflow and our numpy implementation. The pycuda implementation however completes in ~5 seconds which is ~30x faster than our CPU based numpy implementation.

Tensorflow on the other hand is even faster than our implementation at ~1 second. This would most likely be because it internally uses the cuDNN library, which is highly optimized for deep neural network operations.

$ ./pycuda_vgg16.py
processed block1_conv1: inshape: (224, 224, 3), outshape: (224, 224, 64)
processed block1_conv2: inshape: (224, 224, 64), outshape: (224, 224, 64)
processed block1_pool: inshape: (224, 224, 64), outshape: (112, 112, 64)
processed block2_conv1: inshape: (112, 112, 64), outshape: (112, 112, 128)
processed block2_conv2: inshape: (112, 112, 128), outshape: (112, 112, 128)
processed block2_pool: inshape: (112, 112, 128), outshape: (56, 56, 128)
processed block3_conv1: inshape: (56, 56, 128), outshape: (56, 56, 256)
processed block3_conv2: inshape: (56, 56, 256), outshape: (56, 56, 256)
processed block3_conv3: inshape: (56, 56, 256), outshape: (56, 56, 256)
processed block3_pool: inshape: (56, 56, 256), outshape: (28, 28, 256)
processed block4_conv1: inshape: (28, 28, 256), outshape: (28, 28, 512)
processed block4_conv2: inshape: (28, 28, 512), outshape: (28, 28, 512)
processed block4_conv3: inshape: (28, 28, 512), outshape: (28, 28, 512)
processed block4_pool: inshape: (28, 28, 512), outshape: (14, 14, 512)
processed block5_conv1: inshape: (14, 14, 512), outshape: (14, 14, 512)
processed block5_conv2: inshape: (14, 14, 512), outshape: (14, 14, 512)
processed block5_conv3: inshape: (14, 14, 512), outshape: (14, 14, 512)
processed block5_pool: inshape: (14, 14, 512), outshape: (7, 7, 512)
processed flatten: inshape: (7, 7, 512), outshape: (25088,)
processed fc1: inshape: (25088,), outshape: (4096,)
processed fc2: inshape: (4096,), outshape: (4096,)
processed predictions: inshape: (4096,), outshape: (1000,)
predictions:  {'African elephant, Loxodonta africana': 0.9346673, 'tusker': 0.06419284, 'Indian elephant, Elephas maximus': 0.0011378153, 'warthog': 6.193812e-07, 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis': 3.1221467e-07}
computed in 0:00:04.784639

To optimize further, we could also write the max pooling algorithm using CUDA, but the speed up won’t be as pronounced as the conv2d case.

Hopefully this simple implementation helps to demystify how deep learning/ML libraries get such incredible speedups when using GPUs (or TPUs on Google Colab)!

Note: execution was done on a Intel i9-9900X processor with a NVIDIA RTX 2060 GPU. code is available on github.

understanding the k struct

2020-05-25T00:00:00+00:00

typedef char *S, C;
typedef unsigned char G;
typedef short H;
typedef int I;
typedef long long J;
typedef float E;
typedef double F;
typedef void V;
typedef struct k0 {
  signed char m, a, t;
  char u;
  int r;
  union {
    G g;
    H h;
    I i;
    J j;
    E e;
    F f;
    S s;
    struct k0 *k;
    struct {
      J n;
      G G0[1];
    };
  };
} * K;

The above code snippet in my opinion provides a glimpse into the simplicity in design of the APL-like languages, from Ken Iverson’s APL in the 1960s to Arthur Whitney’s a+/k/q/kdb+/shakti.

k0 as defined above (from k.h) is the struct which underpins all data types exposed by k/q. It is a tagged union - a struct that can hold different data types and has a tag (t in this case) to indicate the type a particular instance holds.

The elements e-j inside the union are used to represent the basic/atomic types:

e	sz	n	c	k type(s)	e.g.	value
`g`	1
		-1	b	bool	`1b`
		-4	x	byte	`0x2a`
		-10	c	char	`"a"`
`h`	2	-5	h	short	`98h`
`i`	4
		-6	i	int	`42i`
		-13	m	month	`2020.05m`	months from `2020.01m`
		-14	d	date	`2020.05.23`	days from `2020.01.01`
		-17	u	minute	`12:00`	mins from `00:00`
		-18	v	second	`12:00:00`	secs from `00:00:00`
		-19	t	time	`12:00:00.500`	msecs from `00:00:00.000`
`j`	8
		-7	j	long	`42j`
		-16	n	timespan	`0D18:58:00.729433000`	nsecs from `00:00:00.000000000`
		-12	p	timestamp	`2020.05.23D18:59:29.409153000`	nsecs from `2000.01.01` midnight
`e`	4	-8	e	real	`42.42e`
`f`	8
		-9	f	float	`42.42f`
		-15	z	datetime	`2020.05.23T18:59:29.409`	fractional days from `2020.01.01`
`s`	.
		-11	s	symbol	`hi	pointer to an interned string
		-128		error	`'"error"`

e: element from anonymous union inside k0 struct
sz: size of element in bytes
n: type (value of K->t)
c: char code for type

Creating an atomic K object of type t is straightforward:

K createAtom(I t){
  K x=malloc(sizeof*x);
  x->t=t;  // type
  x->r=0;  // ref count
  return x;}

K createInt(I x){K y=createAtom(-6);y->i=x;return y;}
K createLong(J x){K y=createAtom(-7);y->j=x;return y;}  // ...similarly for other types

As you can see above, members of the unnamed union inside k0 can be accessed as if they were members of the outer struct. Moreover, this works recursively, so even members of the last struct of the union can be accessed directly. For instance, n can be accessed directly as x->n, where x is of type K - a pointer to struct k0. These are called anonymous structs/unions and were standardized in C11 although gcc supported them as a language extension before that.

To store simple lists (i.e. lists of atoms of homogeneous type), the last member of the union is used - the struct with members n and G0. While allocating memory for a K object, sizeof(list) bytes are added in for the dynamically sized array. This is known as the struct hack¹. Code to create a simple list of size n and type t would look something like this:

size_t listMallocSize(I t,J n){
  size_t x=sizeof*(K)0;
  if(n>0)
    switch(t){  // type of a simple list of atoms of type t is negative t
      case 7:x+=n*sizeof(J);break;  // simple list of atoms of int (t=-7) having t=7
      case 6:x+=n*sizeof(I);break;
      // ... and so on for other types
    }
  return x-1;  // -1 to account for G0[1]
}
K createSimpleList(I t,J n){
  K x=malloc(listMallocSize(t,n));
  x->t=t;
  x->r=0;
  x->n=n;  // vector size n
  return x;}
// K intVector = createSimpleList(6,100);
// access yth element of intVector as ((I*)(intVector->G0))[y] - k.h has macros for this

The alternative instead of this hack would be to make G0 a pointer, which would have some drawbacks:

it would require 2 malloc/free calls for allocation/de-allocation of the struct and list.
accessing the list would require a dereference each time.

After simple lists, we have the general list - a list of any heterogeneous types. In this case, G0 would be a list of K objects, i.e. pointers to k0 structs each of which could be of a different type. As we’d expect, operations on this type of list would be slower due to the multiple pointer dereferences and non-contiguous layout of the data. This would also be the rationale behind why k/q tries to convert any general list to a simple list automatically - they are much faster and able to utilize SSE/AVX instructions.

Further along, there are dictionaries, which are simply a pair of conforming lists, i.e. a pair of lists of equal size. The G0 array, similar to a general list would now be a list of 2 K objects each of which is a general/simple list - the key list (access using ((K*)(x->G0))[0]) and the value list (((K*)(x->G0))[1]).

This ultimately leads us to the table - the most important data structure in k/q. A table is a flipped column dictionary. A column dictionary is a dictionary with atomic symbol values as keys and simple/general lists of equal sizes as the values. The flipped part means that this dictionary is transposed (figuratively).

/ a column dictionary with list of symbols as the key list and
/ list of conforming list of longs as the value list
q)show d:`a`b`c!(1 2;3 4;5 6)
a| 1 2
b| 3 4
c| 5 6
q)show t:flip d    / flipped column dictionary - a table
a b c
-----
1 3 5
2 4 6

This fact is also reflected in it the table’s internal representation. The K object for a table is a pointer to its column dictionary (using the self-referencing pointer struct k0 *k). This also shows us why taking the transpose (flip) of a list of conforming lists or a column dictionary is very cheap.

In a similar vein, some of the other data structures are also constructed using the same k0 struct:

keyed tables - dictionary with key and value as simple tables
splayed table - dictionary with key as list of columns and value as its file path symbol
partitioned table - dictionary with key as list of columns and value as a table name symbol

All these diverse data structures supported by a single clever struct - pretty cool IMO.

the size of the last array member of a struct can be kept unspecified in C99, which is known as flexible array members ↩

ipcalc in q

2018-09-07T00:00:00+00:00

I recently learnt about ipcalc, a nifty tool that helps visualize basic IPv4 concepts. I think writing a very basic clone can help ingrain these concepts in one’s brain.

Here’s my attempt in q:

pow:{(*/)y#x}
rp:{x,(y-count x)#" "} / right pad
is:{[n;s](n sublist s)," ",n _ s} / insert space
bv2i:sv[0b;] / boolean vector to integer
i2bv:vs[0b;] / integer to boolean vector
ip2dn:{"."sv string"h"$0x0 vs x} / ip to dot notation
ip2class:{$[128>c:first 0x0 vs x;"A";192>c;"B";224>c;"C";240>c;"D";"E"]}
ip2bdn:{[nm;ip]is[nm+floor nm%8;"."sv raze each string i2bv each 0x0 vs ip]} / ip to binary dot notation
ipcLine:{rp[x;11],color[`blue;rp[y;21]],z}
color:{[c;s]"\033[0;",((`red`orange`blue`purple!string 31 33 34 35)c),"m",s,"\033[0m"}
main:{
  ip:0x0 sv "x"$"H"$"." vs first "/" vs .z.x 0;
  nm:"H"$last "/" vs .z.x 0;
  nmIp:bv2i (nm#1b),(32-nm)#0b;
  wc:bv2i (nm#0b),(32-nm)#1b;
  nIp:bv2i i2bv[nmIp]and i2bv ip;
  bIp:bv2i i2bv[nIp]or i2bv wc;
  hmin:bv2i i2bv[nIp]or (31#0b),1b;
  hmax:bv2i i2bv[bIp]and (31#1b),0b;
  ipclass:ip2class ip;
  -1 ipcLine["Address:";ip2dn ip;color[`orange;ip2bdn[nm;ip]]];
  -1 ipcLine["Netmask:";ip2dn[nmIp]," = ",string nm;color[`red;ip2bdn[nm;nmIp]]];
  -1 ipcLine["Wildcard:";ip2dn wc;color[`orange;ip2bdn[nm;wc]]];
  -1 "=>";
  -1 ipcLine["Network:";ip2dn[nIp],"/",string nm;color[`purple;cb#nmBIp],color[`orange;(cb:("ABCDE"!1 2 3 4 4)ipclass)_ nmBIp:ip2bdn[nm;nIp]]];
  -1 ipcLine["HostMin:";ip2dn hmin;color[`orange;ip2bdn[nm;hmin]]];
  -1 ipcLine["HostMax:";ip2dn hmax;color[`orange;ip2bdn[nm;hmax]]];
  -1 ipcLine["Broadcast:";ip2dn bIp;color[`orange;ip2bdn[nm;bIp]]];
  -1 ipcLine["Hosts/Net:";string pow[2;32-nm]-2;color[`purple;" Class ",string ipclass]];
  -1 "";}

main[]
\\

some sample outputs:

$ q ipcalc.q 12.7.149.12/26 -q
Address:   12.7.149.12          00001100.00000111.10010101.00 001100
Netmask:   255.255.255.192 = 26 11111111.11111111.11111111.11 000000
Wildcard:  0.0.0.63             00000000.00000000.00000000.00 111111
=>
Network:   12.7.149.0/26        00001100.00000111.10010101.00 000000
HostMin:   12.7.149.1           00001100.00000111.10010101.00 000001
HostMax:   12.7.149.62          00001100.00000111.10010101.00 111110
Broadcast: 12.7.149.63          00001100.00000111.10010101.00 111111
Hosts/Net: 62                    Class A

$ q ipcalc.q 216.163.54.22/28 -q
Address:   216.163.54.22        11011000.10100011.00110110.0001 0110
Netmask:   255.255.255.240 = 28 11111111.11111111.11111111.1111 0000
Wildcard:  0.0.0.15             00000000.00000000.00000000.0000 1111
=>
Network:   216.163.54.16/28     11011000.10100011.00110110.0001 0000
HostMin:   216.163.54.17        11011000.10100011.00110110.0001 0001
HostMax:   216.163.54.30        11011000.10100011.00110110.0001 1110
Broadcast: 216.163.54.31        11011000.10100011.00110110.0001 1111
Hosts/Net: 14                    Class C

go write your own now…

references

http://linux-ip.net/html/routing-intro.html#ex-routing-intro-ipcalc

setting up your personal domain and email

2018-07-14T00:00:00+00:00

Instructions to setup a custom domain and email addresses similar to this one.

Setup a user page on gitlab. This is very similar to github pages with the added benefit of being able to use any static site generator instead of being limited to jekyll.

This will create your static website at https://<username>.gitlab.io.
Buy a domain (I used namecheap). This is the only step that costs $$.
Change your DNS servers to cloudflare.
Create DNS records and setup TLS certificates. In my case, I created A records for both arpankapoor.com and www.arpankapoor.com.
Setup DNSSEC. I was intermittently facing DNS resolution issues in certain regions before setting this up.
To redirect all http requests to https, enable Always use HTTPS under the Crypto tab on cloudflare. Also consider enabling Automatic HTTPS Rewrites.
If you’d like to redirect www requests to the non-www version, create a cloudflare page rule.

I’ve setup *www.arpankapoor.com/* to permanently redirect (HTTP 301) to https://arpankapoor.com/$2.
Setup your custom domain email addresses using Mailgun. It processes 10k messages per month for free, which is more than sufficient for my needs.

Thanks to gitlab, cloudflare and mailgun, you’ve now got your custom domain up and running with free hosting, TLS certificates and email!

grokking awk

2018-06-16T00:00:00+00:00

This HN post got me to brush up my awk beyond the basic {print $2,$3} to extract fields from csv-like files. Here are some of my notes from The AWK Programming Language by Alfred Aho, Peter Weinberger and Brian Kernighan.

sequence of pattern-action statements pattern { action }
2 types of data: numbers and strings.
fields in current input line: $1, $2… $0 is the entire line.
if there is no pattern, perform action on every line

if there is no action, print lines that match the pattern

$ echo -e 'alice 20\nbob 30\niyer 24' | awk '$2>20'  # all people aged >20
bob 30
iyer 24

NF - number of fields in current line

$ echo 'a b c d' | awk '{print NF, $1, $NF}'  # print field count, the 1st and last field
4 a d

NR - line number

$ echo -e 'alice 20\nbob 30' | awk '{print NR, $0}'  # prefix with line number
1 alice 20
2 bob 30

simple arithmetic

$ echo 'alice 5.50 22' | awk '{print $1, $2 * $3}'
alice 121

use printf like in C

$ echo 'alice 5.50 22' | awk '{printf("%s $%.2f\n", $1, $2 * $3)}'
alice $121.00

string/regex matching

$ echo -e 'susan 20\nbob 24\nsusie 12' | awk '$1=="susie"'
susie 12
$ echo -e 'susan 20\nbob 24\nsusie 12' | awk '$1 ~ /^su/'  # all lines where the 1st field starts with "su"
susan 20
susie 12

use ||, && and ! as in C

BEGIN,END - special patterns that matches before first line is read and after last line has been processed.

$ echo -e 'susan,20\nbob,24\nsusie,12' | awk 'BEGIN {print "name,age"} {print}'  # add header to csv file
name,age
susan,20
bob,24
susie,12
$ echo -e 'susan 20\nbob 24\nsusie 12' | awk '{sum=sum+$2} END{print sum/NR}' # average age
18.6667

if-else, while and for loops similar to C

$ cat /tmp/tmp
name,age
Alice,20
Bob,30
$ awk -F, '{for(i=1;i<=NF;i+=1)if(NR==1)a[i]=$i;else a[i]=a[i] FS $i}END{for(i=1;i<=NF;i+=1)print a[i]}' /tmp/tmp
name,Alice,Bob
age,20,30

The one-liner above transposes the input csv file, expanded here for readability:

{  # since this action block does NOT have a pattern, it will execute for all lines
  for (i = 1; i <= NF; i += 1)
    if (NR == 1)  # if this is the first line
      a[i] = $i
    else
      a[i] = a[i] FS $i  # concatenate current cell to the ith "column" string separated by the Field Separator
}
  
END { # this action block gets executed after all lines have been processed
  for (i = 1; i <= NF; i += 1)
    print a[i]
}

use length for finding string length

$ echo -e 'name age\nalice 20\nbob 30' | awk '{print length($0)}'  # print length of each line
8
8
6