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implementation: started writing interpreter section; finished CPU-side part; also found error with interpreter
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Daniel 2025-04-27 13:17:35 +02:00
parent e571fa5bd6
commit 101b13e7e7
8 changed files with 278 additions and 57 deletions
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10
package/src/ExpressionExecutorCuda.jl
View File

@ -26,7 +26,10 @@ function interpret_gpu(exprs::Vector{Expr}, X::Matrix{Float32}, p::Vector{Vector
ncols = size(X, 2)
results = Matrix{Float32}(undef, ncols, length(exprs))
# TODO: create CuArray for variables here already, as they never change
# TODO: create CuArray for variables here already, as they never change
# could/should be done even before calling this, but I guess it would be diminishing returns
# TODO: test how this would impact performance, if it gets faster, adapt implementation section
# TODO: create CuArray for expressions here already. They also do not change over the course of parameter optimisation and therefore a lot of unnecessary calls to expr_to_postfix can be save (even though a cache is used, this should still be faster)
for i in 1:repetitions # Simulate parameter tuning -> local search (X remains the same, p gets changed in small steps and must be performed sequentially, which it is with this impl)
results = Interpreter.interpret(exprs, X, p)
@ -41,7 +44,10 @@ function evaluate_gpu(exprs::Vector{Expr}, X::Matrix{Float32}, p::Vector{Vector{
ncols = size(X, 2)
results = Matrix{Float32}(undef, ncols, length(exprs))
# TODO: create CuArray for variables here already, as they never change
# TODO: create CuArray for variables here already, as they never change
# could/should be done even before calling this, but I guess it would be diminishing returns
# TODO: test how this would impact performance, if it gets faster, adapt implementation section
# TODO: create CuArray for expressions here already. They also do not change over the course of parameter optimisation and therefore a lot of unnecessary calls to expr_to_postfix can be save (even though a cache is used, this should still be faster)
for i in 1:repetitions # Simulate parameter tuning -> local search (X remains the same, p gets changed in small steps and must be performed sequentially, which it is with this impl)
results = Transpiler.evaluate(exprs, X, p)

3
package/src/Interpreter.jl
View File

@ -23,7 +23,7 @@ function interpret(expressions::Vector{Expr}, variables::Matrix{Float32}, parame
variableCols = size(variables, 2) # number of variable sets to use for each expression
cudaVars = CuArray(variables)
cudaParams = Utils.create_cuda_array(parameters, NaN32) # column corresponds to data for one expression
cudaExprs = Utils.create_cuda_array(exprs, ExpressionElement(EMPTY, 0)) # column corresponds to data for one expression
cudaExprs = Utils.create_cuda_array(exprs, ExpressionElement(EMPTY, 0)) # column corresponds to data for one expression; TODO: replace this 0 with 'undef' if possible
# put into seperate cuArray, as this is static and would be inefficient to send seperatly to every kernel
cudaStepsize = CuArray([Utils.get_max_inner_length(parameters), size(variables, 1)]) # max num of values per expression; max nam of parameters per expression; number of variables per expression
@ -32,6 +32,7 @@ function interpret(expressions::Vector{Expr}, variables::Matrix{Float32}, parame
# Start kernel for each expression to ensure that no warp is working on different expressions
@inbounds for i in eachindex(exprs)
# TODO: Currently only the first expression gets evaluated. Either use a view on "cudaExprs" to determine the correct expression or extend cudaStepsize to include this information (this information was removed in a previous commit)
kernel = @cuda launch=false fastmath=true interpret_expression(cudaExprs, cudaVars, cudaParams, cudaResults, cudaStepsize, i)
# config = launch_configuration(kernel.fun)
threads = min(variableCols, 128)

35
thesis/chapters/implementation.tex
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@ -103,7 +103,9 @@ While the same expression usually occurs only once, sub-expressions can occur mu
Caching can be applied to both individual sub-expressions as well as the entire expression. While it is unlikely for the whole expression to recur frequently, either as a whole or as part of a larger expression, implementing a cache will not degrade performance and will, in fact, enhance it if repetitions do occur. In the context of parameter optimisation, where the evaluators are employed, expressions will recur, making full-expression caching advantageous. The primary drawback of caching is the increased use of RAM. However, given that RAM is plentiful in modern systems, this should not pose a significant issue.
\section{Interpreter}
Talk about how the interpreter has been developed.
The implementation is divided into two main components, the CPU-based control logic and the GPU-based interpreter as outlined in the concept and design chapter. This section aims at describing the technical details of these components. First the CPU-based control logic will be discussed. This component handles the communication with the GPU and is the entry point which is called by the symbolic regression algorithm. Following this, the GPU-based interpreter will be explored, highlighting the specifics of developing an interpreter on the GPU.
An overview of how these components interact with each other is outlined in Figure \ref{fig:interpreter-sequence}. The parts of this figure will be explained in detail in the following sections.
\begin{figure}
\centering
@ -112,11 +114,36 @@ Talk about how the interpreter has been developed.
\label{fig:interpreter-sequence}
\end{figure}
\subsection{CPU Side}
% main loop; kernel transpiled by CUDA.jl into PTX and then executed
The interpreter gets all expressions that need to be interpreted as an input. Additionally, it needs the variable matrix as well as the parameters for each expression. All expressions are passed to the interpreter as an array of Expr objects, as they are needed for the pre-processing step or frontend. The first loop as seen in Figure \ref{fig:interpreter-sequence} is responsible for sending the expressions to the frontend to be converted into the intermediate representation. After this step, the expressions are in the correct format to be sent to the GPU.
main loop; kernel transpiled by CUDA.jl into PTX and then executed
Before the GPU can start with the interpretation, the data needs to be sent to the GPU. Because the variables are already in matrix form, transferring the data is rather simple. Memory needs to be allocated on the global memory of the GPU and then be copied from RAM into the allocated memory. Allocating memory and transferring the data to the GPU is handled in the background by the CuArray type provided by CUDA.jl.
Because the interpreter must be optimised for parameter optimisation workloads, this step is actually performed before the interpreter is called. The variables never change, as they represent the observed inputs of a system that needs to be modelled by the symbolic regression algorithm. Therefore, it would be wasteful to retransmit the variables for every step of the parameter optimisation part. If they are transmitted once and then reused over the duration of the parameter optimisation part, a lot of time can be saved.
After the variables are transmitted, the parameters also need to be transmitted to the GPU. Unlike the variables, the parameters are stored as a vector of vectors. In order to efficiently transmit the parameters, they also need to be brought in a matrix form. The matrix needs to be of the form $k \times N$ where $k$ is equal to the length of the longest inner vector and $N$ is equal to the length of the outer vector. This ensures that all values can be stored in the matrix. After the parameters have been brought into matrix form, they can be transferred to the GPU the same way the variables are transferred.
Similar to the parameters, the expressions are also stored as a vector of vectors. The outer vector holds each expression while inner vectors hold the expressions in their intermediate representation. Therefore, this vector of vectors also needs to be brought into matrix form the same way the parameters are brought into matrix form. Once this has been done, they are transferred to the GPU. Just like with the variables, the expressions stay the same over the course of the parameter optimisation part. Therefore, they are transferred to the GPU before the interpreter is called, to reduce the amount of unnecessary data transfer.
In addition to the already described data that needs to be sent, two more steps are required that have not been included in the sequence diagram \ref{fig:interpreter-sequence}. The first one is the allocation of global memory for the result matrix. Without this, the kernel would not know where to store the interpretation results. Therefore, enough global memory needs to be allocated to allow storing the results to be retrieved after all kernel executions have finished.
\begin{figure}
\centering
\includegraphics[width=.9\textwidth]{memory_layout_data.png}
\caption{The expressions, variables and parameters as they are stored in the GPUs global memory. Note that while on the CPU they are stored as matrices, on the GPU, they are only three arrays of data. The thick lines represent, where a new column and therefore a new set of data begins.}
\label{fig:memory-layout-data}
\end{figure}
Only raw data can be sent to the GPU, which means information about that data is missing. The matrices are represented as flat arrays, which means they have lost their column and row information. This information needs to be sent separately to let the kernel know the dimensions of the expressions, variables and parameters. Otherwise, the kernel does not know at which memory location the second variable set is stored for example. Figure \ref{fig:memory-layout-data} shows how the data is stored without any information about rows or columns of the matrices. The thick lines help to identify where a new column and therefore a new set of data begins. The GPU however has no knowledge of this and therefore the additional information needs to be transferred to ensure that the data in accessed correctly.
Once all the data is present on the GPU, the CPU can dispatch the kernel for each expression. The dispatch includes the pointers to the location of the data allocated above, as well as the index of the expression to interpret. Because all expressions and parameters are sent to the GPU at once, this index ensures that the kernel knows at which memory location the expression is stored that it needs to interpret and which parameter set it needs to use. After the kernel has finished, the result matrix needs to be read from the GPU and returned to the symbolic regression algorithm.
\subsection{GPU Side}
% Memory access (currently global memory only)
% no dynamic memory allocation like on CPU (stack needs to have fixed size)
Now that the GPU has all the required data in its global ... (something along those lines)
Memory access (currently global memory only)
no dynamic memory allocation like on CPU (stack needs to have fixed size)
\section{Transpiler}
Talk about how the transpiler has been developed (probably largest section, because it just has more interesting parts)

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