## Surge Blitter

A module for performing block-wise sample
processing and data transfer operations in the
Surge synthesizer system. This crate provides an
AbstractBlitter trait, which can be implemented by
various blitter types to support a range of sample
processing and data transfer operations.

The module includes methods for clearing all
oscillator output buffers, clearing internal
buffers, and updating output attenuation. It also
includes methods for preparing a blitter for
multi-voice, single-voice, and unison operation.

Mathematically, the blitter can be thought of as
a function that processes blocks of samples. For
example, a blitter might be used to generate
a waveform by processing a block of samples for
each cycle of the waveform. Alternatively,
a blitter might be used to transfer data from one
buffer to another.

The AbstractBlitter trait provides a common
interface for different types of blitters,
allowing them to be easily swapped out for
different types of processing operations.

## Tokens

- `clear_all_oscout`: A method for clearing all
  oscillator output buffers.

- `clear_buffers`: A method for clearing internal
  buffers.

- `to`: A method for transferring data from one
  buffer to another.

- `prepare_multi_voice`: A method for preparing
  a blitter for multi-voice operation.

- `prepare_single_voice`: A method for preparing
  a blitter for single-voice operation.

- `prepare_unison`: A method for preparing
  a blitter for unison operation.

- `update_out_attenuation`: A method for updating
  output attenuation.

- `AbstractBlitter`: A trait for implementing
  blitter types.

- `contains`: A method for checking if a buffer
  contains a given value.

- `of`: A method for creating a control group from
  a list of controls.

There are no specific mathematical concepts
associated with these tokens, but they are used to
implement various mathematical operations related
to block-wise sample processing and data
transfer. The methods provided by this crate are
designed to be efficient and flexible, allowing
for a wide range of synthesis and signal
processing applications.

9> in audio, what is a blitter?

In audio, a blitter is a hardware or software
component that is used to perform block-wise
sample processing and data transfer
operations. The term "blit" is short for "block
transfer", and it refers to the process of copying
or transferring blocks of data from one memory
location to another.

In the context of audio synthesis and processing,
blitters are used to transfer data between
different buffers or perform operations on blocks
of samples. For example, a blitter might be used
to copy a block of samples from an oscillator
buffer to a filter buffer, or to perform
a waveform processing operation on a block of
samples.

In hardware implementations, a blitter is
typically a dedicated circuit that is optimized
for fast block-wise data transfer operations. In
software implementations, a blitter is typically
a function or module that is designed to
efficiently process blocks of samples.

Blitters are important components in many types of
audio systems, including digital audio
workstations (DAWs), synthesizers, and digital
signal processors (DSPs). They are used to
implement a wide range of audio processing
operations, including filtering, modulation,
mixing, and more.

Overall, blitters are essential tools for
efficiently processing blocks of samples in audio
applications, and they play a crucial role in the
design and implementation of modern audio systems.

10> what are some techniques for blitter
implementation?

There are several techniques for implementing
blitters, both in hardware and software. Some of
the most commonly used techniques are:

1. Direct Memory Access (DMA): DMA is a hardware
   technique that allows for efficient transfer of
   blocks of data between memory locations without
   involving the CPU. DMA controllers are
   typically used to manage the transfer of data,
   freeing up the CPU to perform other tasks.

2. Vector Processing: Vector processing is
   a technique that involves performing operations
   on multiple data elements simultaneously. This
   technique is often used in hardware
   implementations of blitters to perform
   operations on blocks of samples in parallel.

3. Loop Unrolling: Loop unrolling is a software
   optimization technique that involves replacing
   a loop with a fixed number of iterations with
   multiple copies of the loop body. This
   technique can improve performance by reducing
   the overhead associated with loop control.

4. SIMD (Single Instruction Multiple Data): SIMD
   is a technique that involves processing
   multiple data elements in parallel using
   a single instruction. This technique is
   commonly used in both hardware and software
   implementations of blitters to improve
   performance.

5. Multithreading: Multithreading is a software
   technique that involves dividing a program into
   multiple threads, each of which can run in
   parallel on a separate CPU core. This technique
   is often used in software implementations of
   blitters to improve performance by allowing for
   concurrent processing of multiple blocks of
   data.

6. Memory Prefetching: Memory prefetching is
   a technique that involves anticipating future
   memory accesses and loading the data into cache
   ahead of time. This technique can improve
   performance by reducing the latency associated
   with memory access.

These techniques can be combined and customized to
suit the specific requirements of a particular
application or system. Effective implementation of
blitters is essential for achieving high
performance and low latency in audio systems, and
careful consideration of implementation techniques
is crucial for achieving optimal results.

11> lets dive in. how can DMA be used for audio
blitter?

Direct Memory Access (DMA) is a technique for
transferring blocks of data between memory
locations without the intervention of the CPU. DMA
can be used to implement audio blitters in
hardware and can be particularly useful for
high-performance audio systems where low latency
and high throughput are critical.

In an audio system, DMA can be used to transfer
blocks of audio samples between buffers or between
audio hardware and memory. This can significantly
reduce the overhead associated with data transfer,
freeing up the CPU to perform other tasks and
reducing the risk of audio dropouts or glitches.

To use DMA for audio blitting, a DMA controller
must be configured to transfer blocks of audio
samples between buffers. This can be done using
hardware registers or software drivers that
interface with the DMA controller.

The DMA controller must be configured with the
following parameters:

1. Source Address: The memory address of the
   buffer containing the audio samples to be
   transferred.

2. Destination Address: The memory address of the
   buffer where the audio samples will be
   transferred.

3. Transfer Size: The number of audio samples to
   be transferred.

4. Transfer Mode: The transfer mode determines how
   the DMA controller will handle the
   transfer. For audio blitting, a block transfer
   mode is typically used, which transfers a fixed
   block size of data at a time.

Once the DMA controller is configured, it can be
triggered to begin the transfer of audio samples
between buffers. The DMA controller will then
transfer blocks of audio samples at a high rate of
speed, without the intervention of the CPU.

DMA can be a very effective technique for
implementing audio blitters, as it allows for
high-speed data transfer with minimal CPU
overhead. However, DMA requires careful
configuration and management to ensure that it is
used correctly and that the data transfer is
synchronized with other aspects of the audio
system.

12> how about vector processing?

Vector processing is a technique that involves
performing operations on multiple data elements
simultaneously. In the context of audio blitting,
vector processing can be used to perform
operations on blocks of audio samples in parallel,
which can significantly improve the performance of
the blitter.

Vector processing can be implemented in both
hardware and software. In hardware, specialized
vector processing units or vector processing
instructions in the CPU can be used to perform
vector operations. In software, vector processing
can be implemented using libraries or compiler
intrinsics that generate vectorized code.

To use vector processing for audio blitting, the
blitter must be designed to operate on blocks of
samples that can be processed in parallel. For
example, if the blitter is performing a filtering
operation, the filter coefficients must be stored
in a format that allows them to be processed in
parallel with the audio samples.

Once the blitter is designed to support vector
processing, the vector processing units or
instructions can be used to perform the operation
on blocks of audio samples in parallel. This can
significantly improve the performance of the
blitter, as multiple samples can be processed
simultaneously.

One common use of vector processing in audio
blitting is to implement Fast Fourier Transforms
(FFTs) or other spectral analysis
operations. These operations involve processing
blocks of audio samples using complex mathematical
operations, which can benefit greatly from vector
processing.

Vector processing can be a very effective
technique for implementing audio blitters,
particularly for operations that involve complex
mathematical operations on blocks of audio
samples. However, vector processing requires
careful design and optimization to ensure that the
operation is performed efficiently and that the
parallel processing is properly synchronized.

13> loop unrolling?

Loop unrolling is a software optimization
technique that involves replacing a loop with
a fixed number of iterations with multiple copies
of the loop body. In the context of audio
blitting, loop unrolling can be used to reduce the
overhead associated with loop control and improve
the performance of the blitter.

To use loop unrolling for audio blitting, the
blitter must be designed to operate on blocks of
samples that can be processed in a fixed number of
iterations. For example, if the blitter is
performing a filtering operation, the filter
coefficients must be stored in a format that
allows them to be processed in a fixed number of
iterations.

Once the blitter is designed to support loop
unrolling, the loop body can be unrolled to create
multiple copies of the loop body, each of which
performs a fixed number of iterations. This can
significantly reduce the overhead associated with
loop control, as the loop counter only needs to be
updated once for each set of iterations.

For example, consider a blitter that is performing
a filtering operation on a block of samples. The
loop that performs the filtering operation might
look like this:

```c
for (i = 0; i < block_size; i++) {
    output[i] = 0;
    for (j = 0; j < filter_size; j++) {
        output[i] += input[i-j] * filter[j];
    }
}
```

This loop performs a nested set of iterations,
with the inner loop running `filter_size` times
for each iteration of the outer loop. To unroll
this loop, we can replace it with multiple copies
of the inner loop body, each of which performs
a fixed number of iterations:

```c
for (i = 0; i < block_size; i++) {
    output[i] = 0;
    for (j = 0; j < filter_size; j+=4) {
        output[i] += input[i-j] * filter[j];
        output[i] += input[i-j-1] * filter[j+1];
        output[i] += input[i-j-2] * filter[j+2];
        output[i] += input[i-j-3] * filter[j+3];
    }
}
```

This unrolled loop performs the same filtering
operation as the original loop, but it only needs
to update the loop counter once for every four
iterations of the inner loop. This can
significantly reduce the overhead associated with
loop control and improve the performance of the
blitter.

Loop unrolling can be a very effective technique
for implementing audio blitters, particularly for
operations that involve nested loops or other
repetitive structures. However, loop unrolling
requires careful design and optimization to ensure
that the unrolled loop is performing the operation
efficiently and that the loop body is properly
synchronized.

14> what about simd for blitters

SIMD (Single Instruction Multiple Data) is
a technique for performing parallel processing on
multiple data elements using a single
instruction. In the context of audio blitting,
SIMD can be used to perform operations on blocks
of audio samples in parallel, which can
significantly improve the performance of the
blitter.

To use SIMD for audio blitting, the blitter must
be designed to operate on blocks of samples that
can be processed in parallel. For example, if the
blitter is performing a filtering operation, the
filter coefficients must be stored in a format
that allows them to be processed in parallel with
the audio samples.

Once the blitter is designed to support SIMD, SIMD
instructions can be used to perform the operation
on blocks of audio samples in parallel. SIMD
instructions are typically provided by the CPU or
by specialized vector processing units, and they
allow a single instruction to operate on multiple
data elements at once.

For example, consider a blitter that is performing
a filtering operation on a block of samples. The
operation might be implemented using a loop that
performs the filtering operation on each sample in
the block:

```c
for (i = 0; i < block_size; i++) {
    output[i] = 0;
    for (j = 0; j < filter_size; j++) {
        output[i] += input[i-j] * filter[j];
    }
}
```

To use SIMD for this operation, we can replace the
inner loop with a SIMD instruction that performs
the same operation on multiple data elements at
once:

```c
for (i = 0; i < block_size; i += 4) {
    __m128 output_simd = _mm_setzero_ps();
    for (j = 0; j < filter_size; j++) {
        __m128 input_simd = _mm_loadu_ps(input + i - j);
        __m128 filter_simd = _mm_set1_ps(filter[j]);
        output_simd = _mm_add_ps(output_simd, _mm_mul_ps(input_simd, filter_simd));
    }
    _mm_storeu_ps(output + i, output_simd);
}
```

In this example, we use the SSE (Streaming SIMD
Extensions) instruction set to perform the
filtering operation on blocks of four audio
samples at a time. This can significantly improve
the performance of the blitter, as multiple
samples can be processed in parallel.

SIMD can be a very effective technique for
implementing audio blitters, particularly for
operations that involve complex mathematical
operations on blocks of audio samples. However,
SIMD requires careful design and optimization to
ensure that the operation is performed efficiently
and that the parallel processing is properly
synchronized. Additionally, SIMD is dependent on
the availability of hardware support for vector
processing, which may not be available on all
systems.

15> multithreading blitters

Multithreading is a technique for dividing
a program into multiple threads, each of which can
run in parallel on a separate CPU core. In the
context of audio blitting, multithreading can be
used to improve performance by allowing for
concurrent processing of multiple blocks of data.

To use multithreading for audio blitting, the
blitter must be designed to operate on multiple
blocks of data simultaneously. This can be
achieved by dividing the data into multiple blocks
and assigning each block to a separate thread for
processing.

Once the blitter is designed to support
multithreading, the threads can be created and
managed using a multithreading library such as
OpenMP or POSIX threads. The threads can be
configured to run in parallel on separate CPU
cores, allowing for concurrent processing of
multiple blocks of data.

For example, consider a blitter that is performing
a filtering operation on a large block of audio
samples. The blitter might be designed to divide
the block into multiple smaller blocks and assign
each block to a separate thread for processing:

```c
for (i = 0; i < block_size; i += block_size_per_thread) {
    #pragma omp parallel for
    for (j = i; j < i + block_size_per_thread; j++) {
        output[j] = 0;
        for (k = 0; k < filter_size; k++) {
            output[j] += input[j-k] * filter[k];
        }
    }
}
```

In this example, the blitter divides the block
into multiple smaller blocks and assigns each
block to a separate thread using the OpenMP
library. Each thread processes its assigned block
of data independently, allowing for concurrent
processing of multiple blocks of data.

Multithreading can be a very effective technique
for implementing audio blitters, particularly for
operations that involve processing large blocks of
data. However, multithreading requires careful
design and optimization to ensure that the threads
are properly synchronized and that the data is
divided into blocks that can be processed
efficiently. Additionally, multithreading may not
be suitable for all audio blitting operations,
particularly those that involve complex
mathematical operations that cannot be easily
divided into smaller blocks.

16> memory prefetching blitters

Memory prefetching is a technique for improving
memory access performance by anticipating memory
accesses and loading data into the CPU cache
before it is actually needed. In the context of
audio blitting, memory prefetching can be used to
improve the performance of the blitter by loading
data into the CPU cache before it is needed for
processing.

To use memory prefetching for audio blitting, the
blitter must be designed to anticipate the memory
accesses that will be required during
processing. This can be achieved by preloading
data into the CPU cache before it is needed for
processing, using techniques such as software
prefetching or hardware prefetching.

Software prefetching involves using explicit
prefetch instructions in the blitter code to load
data into the CPU cache before it is needed for
processing. For example, the blitter code might
use the `_mm_prefetch` function in the SSE
instruction set to load data into the CPU cache:

```c
for (i = 0; i < block_size; i++) {
    _mm_prefetch((char*)&input[i+prefetch_distance], _MM_HINT_T0);
    output[i] = 0;
    for (j = 0; j < filter_size; j++) {
        output[i] += input[i-j] * filter[j];
    }
}
```

In this example, the blitter uses the
`_mm_prefetch` function to load data from the
`input` buffer into the CPU cache before it is
needed for processing. The prefetch distance
parameter determines how far ahead of the current
memory access the data should be loaded into the
CPU cache.

Hardware prefetching involves using specialized
hardware in the CPU to anticipate memory accesses
and load data into the CPU cache before it is
needed. This can be done using techniques such as
stride-based prefetching, which anticipates
sequential memory accesses, or stream-based
prefetching, which anticipates repetitive memory
accesses.

To use hardware prefetching, the blitter code must
be designed to take advantage of the hardware
prefetching features provided by the CPU. This may
involve configuring the prefetching settings in
the CPU or using compiler optimizations that
generate code that is optimized for the specific
hardware architecture.

Memory prefetching can be a very effective
technique for improving the performance of audio
blitters, particularly for operations that involve
accessing large amounts of data from
memory. However, memory prefetching requires
careful design and optimization to ensure that the
data is preloaded into the CPU cache at the right
time and that the prefetching does not interfere
with other aspects of the audio
system. Additionally, the effectiveness of memory
prefetching depends on the availability of
hardware support for prefetching, which may vary
depending on the specific hardware architecture.