# Beginnings of a liveness analysis

## Yesterday, and the plan for today

Yesterday, I made an improvement for the dynamic allocator in Futhark. I only changed opencl.h though, and forgot to update cuda.h with the new free_list_find, so that has been fixed today.

The plan today is to get a grip on the linear scan algorithm and figure out what I need in order to implement it for Futhark.

When originally reading up on the linear scan register allocation algorithm, I made notes that I never got around to publish. I should read through those again. In case I learn something new, I'll try to write it down here.

Then, I'll make a broad plan for how I'm going to attack implementing the allocation algorithm in Futhark. I believe it's going to start with implementing a liveness analysis. Perhaps preceeded by some investigation into what kind of liveness analysis I should use.

I also need to print the ICFP paper I'm supposed to artifact-review.

## Futhark and the linear scan register allocation algorithm

Actually, let's just insert my notes from the previous reading of the linear scan algorithm paper here. They serve as a good explanation of the basic problem we're trying to solve, how the linear scan algorithm works, and some initial thoughts on how to adapt the algorithm to work for our use case.

### What is the problem that we're trying to solve

We want to try to minimize the number of shared memory allocations in kernels.

For instance, consider the following code snippet written in OpenCL:

__kernel void
lud_perimeter(__global float *m,
__local  float *peri_row,
__local  float *peri_col,
int matrix_dim,
int offset)
{
int i,j, array_offset;
int idx;

int  bx = get_group_id(0);
int  tx = get_local_id(0);

array_offset = offset*matrix_dim+offset;

for (i=0; i < BLOCK_SIZE; i++) {
peri_row[i * BLOCK_SIZE+ idx]=m[array_offset+(bx+1)*BLOCK_SIZE+idx];
array_offset += matrix_dim;
}

for (i=0; i < BLOCK_SIZE; i++) {
peri_col[i * BLOCK_SIZE + idx] = m[array_offset+idx];
array_offset += matrix_dim;
}

/* Some code that uses peri_row */

/* Some code that uses peri_col */

}


Seeing that the uses peri_row and peri_col never overlap, it might be beneficial to instead generate something like the following:

__kernel void
lud_perimeter(__global float *m,
__local  float *peri,
int matrix_dim,
int offset)
{
int i,j, array_offset;
int idx;

int  bx = get_group_id(0);
int  tx = get_local_id(0);

array_offset = offset*matrix_dim+offset;

for (i=0; i < BLOCK_SIZE; i++) {
peri[i * BLOCK_SIZE + idx]=m[array_offset+(bx+1)*BLOCK_SIZE+idx];
array_offset += matrix_dim;
}

/* Some code that uses peri as peri_row */

for (i=0; i < BLOCK_SIZE; i++) {
peri[i * BLOCK_SIZE + idx] = m[array_offset+idx];
array_offset += matrix_dim;
}

/* Some code that uses peri as peri_col */

}


Instead of having two local (shared) memory allocations, peri_col and peri_row, we only use one called peri.

Of course, that is OpenCL code, so what would it look like in Futhark?

entry mapscan [k] (n: i32) (xs: [k]i32)
: [k]i32 =
let m = k / n
let xss' = unflatten m n xs
let xss =
map2 (\row i ->
let res1 =
loop row for _ in 0..<61 do
let row' = map (+i) row
in scan (+) 0 row'

let res2 =
loop row for _ in 0..<62 do
let row' = map (+i) row
in scan (+) 1 row'

in map2 (+) res1 res2
) xss' (iota m)
in flatten_to k xss


Compiling this to the explicit allocations IR in Futhark produces the following piece of code:

let {i64 binop_x_6072} = sext i32 n_5315 to i64
let {i64 double_buffer_size_6073} = mul64(4i64, binop_x_6072)
let {i64 binop_x_6077} = sext i32 n_5315 to i64
let {i64 double_buffer_size_6078} = mul64(4i64, binop_x_6077)

...

let {[m_5320][n_5315]i32 res_5440} =
segmap_group
(#groups=m_5320; groupsize=n_5315)
(gtid_5359 < m_5320) (~phys_tid_5371) : {[n_5315]i32} {
-- x_5441 aliases res_coalesced_5871
-- x_5441 : [n_5315]i32@@mem_5946->{base: [n_5315, m_5320]; contiguous: False; LMADs: [{offset: gtid_5359; strides: [m_5320]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 x_5441} = res_coalesced_5871[gtid_5359,
0i32:+n_5315*1i32]
let {mem@local double_buffer_mem_6070} =
alloc(double_buffer_size_6073, @local)
-- x_linear_double_buffer_copy_6074 : [n_5315]i32@@double_buffer_mem_6070->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 x_linear_double_buffer_copy_6074} = copy(x_5441)
let {mem@local mem_5955} =
alloc(bytes_5948, @local)
-- res1_5443 : [n_5315]i32@@double_buffer_mem_6070->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 res1_5443} =
-- Consumes x_linear_double_buffer_copy_6074
-- row_5444 : *[n_5315]i32@@double_buffer_mem_6070->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
loop {*[n_5315]i32 row_5444} = {x_linear_double_buffer_copy_6074}
for i_5445:i32 < 61i32 do {
-- res_5446 : [n_5315]i32@@mem_5955->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 res_5446} =
(#groups=m_5320; groupsize=n_5315)
(fn {i32} (i32 x_5447, i32 x_5448) =>
let {i32 res_5449} = add32(x_5447, x_5448)
in {res_5449},
{0i32})
(gtid_5362 < n_5315) (~phys_tid_5363) : {i32} {
let {i32 x_5450} = row_5444[gtid_5362]
let {i32 res_5451} =
<range_valid_c_5327>
return {returns res_5451}
}
-- double_buffer_array_6071 : [n_5315]i32@@double_buffer_mem_6070->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 double_buffer_array_6071} = copy(res_5446)
in {double_buffer_array_6071}
}
let {mem@local double_buffer_mem_6075} =
alloc(double_buffer_size_6078, @local)
-- x_linear_double_buffer_copy_6079 : [n_5315]i32@@double_buffer_mem_6075->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 x_linear_double_buffer_copy_6079} = copy(x_5441)
let {mem@local mem_5965} =
alloc(bytes_5948, @local)
-- res2_5452 : [n_5315]i32@@double_buffer_mem_6075->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 res2_5452} =
-- Consumes x_linear_double_buffer_copy_6079
-- row_5453 : *[n_5315]i32@@double_buffer_mem_6075->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
loop {*[n_5315]i32 row_5453} = {x_linear_double_buffer_copy_6079}
for i_5454:i32 < 62i32 do {
-- res_5455 : [n_5315]i32@@mem_5965->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 res_5455} =
(#groups=m_5320; groupsize=n_5315)
(fn {i32} (i32 x_5456, i32 x_5457) =>
let {i32 res_5458} = add32(x_5456, x_5457)
in {res_5458},
{1i32})
(gtid_5364 < n_5315) (~phys_tid_5365) : {i32} {
let {i32 x_5459} = row_5453[gtid_5364]
let {i32 res_5460} =
<range_valid_c_5327>
return {returns res_5460}
}
-- double_buffer_array_6076 : [n_5315]i32@@double_buffer_mem_6075->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 double_buffer_array_6076} = copy(res_5455)
in {double_buffer_array_6076}
}
let {mem@local mem_5969} =
alloc(bytes_5948, @local)
-- res_5461 : [n_5315]i32@@mem_5969->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 res_5461} =
(#groups=m_5320; groupsize=n_5315)
(gtid_5366 < n_5315) (~phys_tid_5367) : {i32} {
let {i32 x_5462} = res1_5443[gtid_5366]
let {i32 x_5463} = res2_5452[gtid_5366]
let {i32 res_5464} = add32(x_5462, x_5463)
return {returns res_5464}
}
return {returns res_5461}
}
in {mem_5974, res_5440}


Here, we can see that two copies of x_5441 are performed:

let {mem@local double_buffer_mem_6070} =
alloc(double_buffer_size_6073, @local)
-- x_linear_double_buffer_copy_6074 : [n_5315]i32@@double_buffer_mem_6070->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 x_linear_double_buffer_copy_6074} = copy(x_5441)

...

let {mem@local double_buffer_mem_6075} =
alloc(double_buffer_size_6078, @local)
-- x_linear_double_buffer_copy_6079 : [n_5315]i32@@double_buffer_mem_6075->{base: [n_5315]; contiguous: True; LMADs: [{offset: 0i32; strides: [1i32]; rotates: [0i32]; shape: [n_5315]; permutation: [0]; monotonicity: [Inc]}]}
let {[n_5315]i32 x_linear_double_buffer_copy_6079} = copy(x_5441)


Both 6074 and 6079 are only read from, they are the same size, and they dont overlap. In principle, we should be able to avoid both having to alloc twice, and having to copy twice. The purpose of using the linear scan register allocation is to avoid the extra allocation1. Later, we'll have to investigate how to avoid the copy.

### What is the problem that register allocation is trying to solve?

Register allocation is the problem of assigning a limited number of registers to an arbitrary number of values. In CPUs, a register is the type of memory that is closest to the execution, meaning that accessing a register in order to perform some operation is orders of magnitude faster than accessing memory (RAM) to perform the same operation. Therefore, to make our programs run fast, we need make use of registers in an efficient manner. However, the number of registers is usually fairly limited; x86 for instance, only exposes 16 registers to the user. Therefore, if we have more than 16 variables in our program, we'll need to manage which variables reside in registers and which are "spilled" to memory at any given point in time. Common approaches to solving register allocation use some sort of liveness analysis to determine which variables are live at the same time and therefore need to co-exist. If two variables are not live at the same time, they can reuse each others' registers.

The first algorithms for register allocation used graph coloring, and were able to make strong guarantees about the register allocation produced. Unfortunately, graph coloring is NP-complete, so using the graph coloring algorithms was slow, especially as the number of variables increased. In 1999, an alternative technique was proposed by Poletto and Sarkar: linear scan register allocation.

### How does linear scan register allocation solve register allocation

Lhe linear scan register allocation algorithm assumes a list of live-intervals, corresponding to the first and last use of each variable in the given program. The list is sorted in increasing first-use order, which the algorithm then loops through. For each first-use of a variable, it firsts frees registers allocated to variables that are no longer in use, and then allocates a free register to the new variable. If there are no free registers, it spills the variable that has the latest last-use (either the current variable to insert, or the last in the list of intervals).

### How can we use linear scan register allocation to solve our problems

The linear scan algorithm as devised by Poletto and Sarkar applies to registers, which are all more or less interchangable. We are interested in applying it to memory allocations which are not interchangable: They have sizes. On the other hand, we're not in theory limited in how many allocations we can have, we just want to minimize the total number of allocations as much as possible. Since in practice GPUs have limited memory, it would also be beneficial if we could limit the total accumulated size of all the allocations.

So, in order for us to apply the linear scan algorithm, we'd need to keep track of the size of each allocation, in addition to its live-interval. Then, when a variable reaches its last-use, we can put the allocation into a list of free allocations, with the size information. When a new variable is introduced, we look through the list of free allocations in order to find one that fits. If none were found, we perform a new allocation.

After the initial implementation, we can try to improve it. For instance, if we have two non-overlapping live-intervals, with the first requiring a smaller allocation than the latter, they cannot share the allocation, but if we know that they don't overlap, we can allocate the larger memory from the beginning in order to let them share that allocation.

### Complications

What if we don't know the size if the allocations at compile time? Perhaps one has size $$f(x)$$ and another has size $$g(x)$$, where $$f$$ and $$g$$ are some functions of $$x$$, or even worse, perhaps one has size $$x$$ and another has size $$y$$, where $$x$$ and $$y$$ are unrelated? In the former case, we can probably perform some symbolic arithmetic to determine which is larger. In the latter, we cannot do anything, except perhaps a dynamic check? But, if there are a lot of available arrays, all with unrelated sizes to check at runtime, the cost of doing so might outweigh the cost of performing the allocation.

Also, what happens at the end of blocks? Are memory allocations that have been performed inside a block available to later code outside that block? Probably not, unless the block returns the array.

## What's the plan

In broad terms, I need to implement some sort of liveness analysis for the explicit memory IR first. In order to do that, I should try and investigate common methods for performing liveness analysis, in order to find one that's a good fit for our use case.

To begin with, it's probably easiest if I create the liveness analysis in a separate repo, sort of like Roberts AD implementation.

After having created a liveness analysis, it's possible that I can do a simple implementation of the linear scan algorithm that only looks at the literal sizes and doesn't try to do any dynamic checking or symbolic arithmetic to determine which sizes are compatible.

After that, I'll have to investigate both how to use symbolic arithmetic to improve the algorithm (Cosmin mentioned that there already is some code to do these kinds of calculations), and whether I should extend it using some sort of dynamic analysis.

## Liveness analysis

Torben Mogensen has a chapter on register allocation in his book Basics of Compiler Design, which also includes a section about liveness analysis. He uses a bottom-up fix-point analysis which for each line keeps track of which variables are generated and killed, in order to compute in and out, which variables are live at the start and end of each instruction, respectively. The Wikipedia page on liveness analysis seems to use the same basic technique.

Of course, we're not so much interested in each variable, as we are interested in the underlying allocation block. When a mem is returned from an if-statement, it is assigned to a new variable, but the underlying allocation stays the same, and is live throughout. This means that we need to keep track of the underlying allocations, we need to be able to uniquely identify them, and know which variables use which memory blocks.

### Well begun is halfway done

Perhaps a good first program would be one that works through a given explicit memory IR and outputs for each statement, the name of the underlying blocks that are referenced.

Let's first get a skeleton up and running. I have no real experience with writing Futhark passes, so I'll use Roberts AD implementation as inspiration.

The main entry point in his code is the grad function, which takes a Futhark Pass and weaves it into a Futhark compilation pipeline. We'll need to do something similar, only we'll be operating on the KernelsMem representation instead of SOACS. A Futhark pipeline consists of a sequence of Pass. Creating a Futhark compilation pipeline is done using the passes and onePass functions from Futhark.Pipeline. passes is for passes within one representation, while onePass can be used to transform the IR from one representation to another. In addition, Futhark.Passes defines a set of pre-defined pipelines, which can be used to create your own.

Ah, but we don't need to do a pass, for now. I'm really just interested in writing a function like this, where Liveness is a mapping from SubExp to a live range of some sort:

liveness :: FunDef KernelsMem -> Liveness


For now, I've started implementing it as an Action which can be applied at the end of a pipeline. I've uploaded the initial skeleton for the liveness analysis into its own repository here.

## Miscellaneous

### Org mode issues

I fixed the overflow issue in source blocks on this blog pointed out by xiaomat by adding the following piece of code to the project project specification:

:html-head "<style type=\"text/css\">
/*<![CDATA[*/
pre.src { overflow: auto; }
/*]]>*/
</style>"


For some reason, it's set "visible" by default…

Also, adding source code colouring was surprisingly easy, just a matter of install htmlize from melpa.

### Ormolu, nix, setting up a new project

I've tried, and failed, a bunch of times, to find the meaning in Christine Dodrills elaborate nix setup. Today, while trying to set up a fresh Haskell project for the liveness analysis, part of it made sense. Having already set up direnv and lorri in the past, I never really understood the need for niv. But I wanted to set up a nice Nix-based development environment for futhark-liveness, including using ormolu, the Haskell formatter we've been considering for Futhark. It turns out that nixpkgs doesn't have the latest release of ormolu, but by using niv, I could easily install it straight from Github.

Oh, and I got sidetracked badly when I wanted to write a default.nix that could automatically overwrite the Futhark haskell package with a newer one from Github.

## Tomorrow

• Continue with the liveness analysis. The immediate next step is to be able to get some type information inside the livenessFun I've currently defined, since I'm only interested in memory allocations. Then, I'll have to start implementing the actual liveness analysis, using Torbens algorithm as inspiration.

## Issues

• From the linear scan algorithm article: "Another is depth-first ordering, the reverse of the order in which nodes are last visited in a preorder traversal of the flow graph [Aho et al. 1986]." I don't know what the "flow graph" is. I should try to write an example to get an understanding of what they mean by this sentence. Also, what are nodes, in this context? Variables?
• How to handle aliasing? In the expression let xs' = xs in ..., xs' refers to the same memory block as xs, so even though xs might not be live any more, the underlying memory block is still live.

## Footnotes:

1

Reusing the same allocation is called "coalescing", in the context of register allocation.