This project aims to provide a lightweight runtime to semi-automatically optimize data flow pipelines for locality. Pipelines are specified as graphs of operators processing data between multi-dimensional buffers. Slinky then allows the user to describe transformations to the pipeline that improve memory locality and reduce memory usage by executing small crops of operator outputs.

Slinky is heavily inspired and motivated by Halide. It can be described by starting with Halide, and making the following changes:

• Slinky is a runtime interpreter, not a compiler.
• All operations that read and write buffers are user defined callbacks (except copies).
• Bounds for each operation are manually provided instead of inferred as in Halide.

Because we are not responsible for generating the inner loop code like Halide, scheduling is a dramatically simpler problem. Without needing to worry about instruction selection, register pressure, and so on, the cost function for scheduling is a much more straightforward function of high level memory access patterns.

The ultimate goal of Slinky is to make automatic scheduling of pipelines reliable and fast enough to implement a just-in-time optimization engine at runtime.

Pipelines are described by operators called funcs and connected by buffer_exprs. func has a list of input and output objects. A func can have multiple outputs, but all outputs must be indexed by one set of dimensions for the func. An input or output is associated with a buffer_expr. An output has a list of dimensions, which identify variables (var) used to index the corresponding dimension of the buffer_expr. An input has a list of bounds expressions, expressed as an inclusive interval [min, max], where the bounds can depend on the variables from the output dimensions.

The actual implementation of a func is a callback taking a single argument eval_context. This object contains the state of the program at the time of the call. Values of any symbol currently in scope at the time of the call can be accessed in the eval_context.

Here is an example of a simple pipeline of two 1D elementwise funcs:

node_context ctx;

auto in = buffer_expr::make(ctx, "in", sizeof(int), 1);
auto out = buffer_expr::make(ctx, "out", sizeof(int), 1);
auto intm = buffer_expr::make(ctx, "intm", sizeof(int), 1);

var x(ctx, "x");

func mul = func::make(multiply_2, {in, {point(x)}}, {intm, {x}});
func add = func::make(add_1, {intm, {point(x)}}, {out, {x}});

pipeline p = build_pipeline(ctx, {in}, {out});

• in and out are the input and output buffers.
• intm is the intermediate buffer between the two operations.
• To describe this pipeline, we need one variable x.
• Both func objects have the same signature:
  • Consume a buffer of const int, produce a buffer of int.
  • The output dimension is indexed by x, and both operations require a the single point interval [x, x] of their inputs.
  • multiply_2 and add_1 are functions implementing this operation.

The possible implementations of this pipeline vary between two extremes:

1. Allocating intm to have the same size as out, and executing all of mul, followed by all of add.
2. Allocating intm to have a single element, and executing mul followed by add in a single loop over the output elements.

Of course, (2) would have extremely high overhead, and would not be a desireable strategy. If the buffers are large, (1) is inefficient due to poor memory locality. The ideal strategy is to split out into chunks, and compute the two operations at each chunk. This allows targeting a chunk size that fits in the cache, but amortizes the overhead of setting up the buffers and calling the functions implementing this operation. This can be implemented with the following schedule:

const int chunk_size = 8;
add.loops({x, chunk_size});
mul.compute_at({&stencil, x});

In this case, the mul.compute_at specification is only for illustration purposes, it is equivalent to the default behavior, which is to compute funcs at the innermost location that does not imply redundant compute.

This will result in a slinky program that looks like this:

intm = allocate(heap, 4, {
  {[buffer_min(out, 0), buffer_max(out, 0)], 4, 8}
}) {
 intm.uncropped = clone_buffer(intm) {
  serial loop(x in [buffer_min(out, 0), buffer_max(out, 0)], 8) {
   crop_dim(intm, 0, [x, min((x + 7), buffer_max(out, 0))]) {
    call(add, {in}, {intm})
   }
   crop_dim(out, 0, [x, (x + 7)]) {
    call(mul, {intm.uncropped}, {out})
   }
  }
 }
}

Observations:

• The loop steps by 8 elements at a time, as we specified in the schedule.
• Within the loop, we call add followed by mul, inside crops that restrict the computations to (up to) 8 elements at a time (this pipeline can handle any number of output elements, it is not limited to be a multiple of 8).
• The allocation is "folded" by 8, limiting the size of the allocation to only what is needed for each loop iteration.

Here is an example of a pipeline that has a stage that is a stencil, such as a convolution:

node_context ctx;

auto in = buffer_expr::make(ctx, "in", sizeof(short), 2);
auto out = buffer_expr::make(ctx, "out", sizeof(short), 2);

auto intm = buffer_expr::make(ctx, "intm", sizeof(short), 2);

var x(ctx, "x");
var y(ctx, "y");

func add = func::make(add_1, {in, {point(x), point(y)}}, {intm, {x, y}});
func stencil =
    func::make(sum3x3, {intm, {{x - 1, x + 1}, {y - 1, y + 1}}}, {out, {x, y}});

pipeline p = build_pipeline(ctx, {in}, {out});

• in and out are the input and output buffers.
• intm is the intermediate buffer between the two operations.
• We need two variables x and y to describe the buffers in this pipeline.
• The first stage add is an elementwise operation that adds one to each element.
• The second stage is a stencil sum3x3, which computes the sum of the 3x3 neighborhood around x, y.
• The output of both stages is indexed by x, y. The first stage is similar to the previous elementwise example, but the stencil has bounds [x - 1, x + 1], [y - 1, y + 1].

The typical way that many systems would execute such a pipeline is to run all of add_1, followed by all of sum3x3, storing the intermediate result in a buffer equal to the size of the input. Here is a visualization of this strategy.

An interesting way to implement this pipeline is to compute rows of out at a time, keeping the window of rows required from add in memory. This can be expressed with the following schedule:

stencil.loops({y});
add.compute_at({&stencil, y});

This means:

• We want a loop over y, instead of just passing the whole 2D buffer to sum3x3.
• We want to compute add at that same loop over y to compute stencil.

This generates a program like so:

intm = allocate<intm>({
  {[(buffer_min(out, 0) + -1), (buffer_max(out, 0) + 1)], 2},
  {[(buffer_min(out, 1) + -1), (buffer_max(out, 1) + 1)], ((buffer_extent(out, 0) * 2) + 4), 3}
} on heap) {
 loop(y in [(buffer_min(out, 1) + -2), buffer_max(out, 1)]) {
   crop_dim<1>(intm, [(y + 1), (y + 1)]) {
   call(add, {in}, {intm})
  }
  crop_dim<1>(out, [y, y]) {
   call(sum3x3, {intm}, {out})
  }
 }
}

This program does the following:

• Allocates a buffer for intm, with a fold factor of 3, meaning that the coordinates of the second dimension are modulo 3 when computing addresses.
• Runs a loop over y starting from 2 rows before the first output row, calling add and sum3x3 at each y.
• The calls are cropped to the line to be produced on the current iteration y. sum3x3 reads rows y-1, y, and y+1 of intm, so we need to produce y+1 of intm before producing y of out.
• The intm buffer persists between loop iterations, so we only need to compute the newly required line y+1 of intm on each iteration, lines y-1 and y were already produced on previous iterations.
• Because we started the loop two iterations of y early, lines y-1 and y have already been produced for the first value of y of out. For these two "warmup" iterations, the sum3x3 call's crop of out will be an empty buffer (because crops clamp to the original bounds).
• Because we only need lines [y-1,y+1], we can "fold" the storage of intm, by rewriting all accesses y to be y%3.

Here is a visualization of this strategy.

Here is a more involved example, which computes the matrix product d = (a x b) x c:

node_context ctx;

auto a = buffer_expr::make(ctx, "a", sizeof(float), 2);
auto b = buffer_expr::make(ctx, "b", sizeof(float), 2);
auto c = buffer_expr::make(ctx, "c", sizeof(float), 2);
auto abc = buffer_expr::make(ctx, "abc", sizeof(float), 2);

auto ab = buffer_expr::make(ctx, "ab", sizeof(float), 2);

var i(ctx, "i");
var j(ctx, "j");

// The bounds required of the dimensions consumed by the reduction depend on the size of the
// buffers passed in. Note that we haven't used any constants yet.
auto K_ab = a->dim(1).bounds;
auto K_abc = c->dim(0).bounds;

// We use float for this pipeline so we can test for correctness exactly.
func matmul_ab =
    func::make<const float, const float, float>(matmul<float>, {a, {point(i), K_ab}}, {b, {K_ab, point(j)}}, {ab, {i, j}});
func matmul_abc = func::make<const float, const float, float>(
    matmul<float>, {ab, {point(i), K_abc}}, {c, {K_abc, point(j)}}, {abc, {i, j}});
	
pipeline p = build_pipeline(ctx, {a, b, c}, {abc});

• a, b, c, abc are input and output buffers.
• ab is the intermediate product a x b.
• We need 2 variables i and j to describe this pipeline.
• Both func objects have the same signature:
  • Consume two operands, produce one operand.
  • The first func produces ab, the second func consumes it.
  • The bounds required by output element i, j of the first operand is the ith row and all the columns of the first operand. We use dim(1).bounds of the first operand, but dim(0).bounds of the second operand should be equal to this.
  • The bounds required of the second operand is similar, we just need all the rows and one column instead. We use dim(0).bounds of the second operand to avoid relying on the intermediate buffer, which will have its bounds inferred (maybe this would still work...).
  • matmul is the callback function implementing the matrix multiply operation.

Much like the elementwise example, we can compute this in a variety of ways between two extremes:

1. Allocating ab to have the full extent of the product a x b, and executing all of the first multiply followed by all of the second multiply.
2. Each row of the second product depends only on the same row of the first product. Therefore, we can allocate ab to hold only one row of the product a x b, and compute both products in a loop over rows of the final result.

In practice, matrix multiplication kernels like to produce multiple rows at once for maximum efficiency.

We expect this approach to fill a gap between two extremes that seem prevalent today (TODO: is this really true? I think so...):

1. Pipeline interpreters that execute entire operations one at a time.
2. Pipeline compilers that generate code specific to a pipeline.

We expect Slinky to execute suitable pipelines using less memory than (1), but at a lower performance than what is possible with (2). We emphasize possible because actually building a compiler that does this well on novel code is very difficult. We think Slinky's approach is a more easily solved problem, and will degrade more gracefully in failure cases.

This performance app attempts to measure the overhead of interpreting pipelines at runtime. The test performs a copy between two 2D buffers of "total size" bytes twice: first to an intermediate buffer, and then to the output. The inner dimension of size "copy size" is copied with memcpy, the outer dimension is a loop implemented in one of two ways:

1. An "explicit loop" version, which has a loop in the pipeline for the outer dimension (interpreted by Slinky).
2. An "implicit loop" version, which loops over the outer dimension in the callback.

Two factors affect the performance of this pipeline:

• Interpreter and dispatching overhead of slinky.
• Locality of the copy operations.

This is an extreme example, where memcpy is the fastest operation (per memory accessed) that could be performed in a pipeline. In other words, this is an upper bound on the overhead that could be expected for an operation on the same amount of memory.

On my machine, here are some data points from this pipeline:

As we should expect, the observations vary depending on the total size of the copy:

• When the total size is small enough to fit in L1 or L2 cache, the cost of the memcpy will be small, and the overhead will be relatively more expensive. This cost is as much as 40% when copying 1 KB at a time, according to the data above.
• Even when the entire copy fits in the L1 cache, the overhead of dispatching 8KB at a time is negligible.
• When the copies are very large, dispatch overhead is insignificant relative to locality improvements.