Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 1, February 2018, pp. 70~75
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i1.pp70-75  70
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Optimizing Apple Lossless Audio Codec Algorithm using
NVIDIA CUDA Architecture
Rafid Ahmed, Md. Sazzadul Islam, Jia Uddin
Department of Computer Science & Engineering, BRAC University, Bangladesh
Article Info ABSTRACT
Article history:
Received May 4, 2017
Revised Oct 3, 2017
Accepted Oct 17, 2017
As majority of the compression algorithms are implementations for CPU
architecture, the primary focus of our work was to exploit the opportunities
of GPU parallelism in audio compression. This paper presents an
implementation of Apple Lossless Audio Codec (ALAC) algorithm by using
NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework.
The core idea was to identify the areas where data parallelism could be
applied and parallel programming model CUDA could be used to execute the
identified parallel components on Single Instruction Multiple Thread (SIMT)
model of CUDA. The dataset was retrieved from European Broadcasting
Union, Sound Quality Assessment Material (SQAM). Faster execution of the
algorithm led to execution time reduction when applied to audio coding for
large audios. This paper also presents the reduction of power usage due to
running the parallel components on GPU. Experimental results reveal that we
achieve about 80-90% speedup through CUDA on the identified components
over its CPU implementation while saving CPU power consumption.
Keyword:
ALAC
Audio decoding
Audio encoding
CUDA
PCM
SIMT
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Jia Uddin,
Department of Computer Science & Engineering,
BRAC University.
66 Mohakhali, Dhaka 1212, Bangladesh.
Email: jia.uddin@bracu.ac.bd
1. INTRODUCTION
Best use of expensive computing resources such as memory, network bandwidth or processing
units is growing day by day. As a result, consumption of those resources needs to be carefully planned
in order to achieve maximum performance. Data compression helps to utilize space limited resources
more effectively. There are several algorithms on lossless audio codecs; Free Lossless Audio Codec
(FLAC) [1], Shorten [2], Windows Media Audio (WMA) [3], MPEG-4 [4], WavPack, Monkey’s Audio
[5] and others being used by programs to alleviate space usage. Lossless audio compression detects and
discards statistical redundancy in audio file in order to get reduced bit-rate and this process is generally
obtained by implementing linear prediction for redundancy removal and the entropy encoder for coding the
predictor output [6]. There are also some tradeoffs on the decision of using compression. One of the
main issues is increase in encoding/decoding time as well as growth of power consumption.
Apple Lossless Audio Codec (ALAC) is an audio coding format developed by Apple Inc. for
lossless data compression of digital music. Filename extension .m4a is used for Apple Lossless data to store
within an MP4 container. This extension is also used by Apple for lossy AAC audio data which is not a
variant of ALAC [7]. An iTunes .m4p file is a DRM-encrypted M4A which isn’t interoperable outside of the
Apple ecosystem. As a result, other lossless codecs, such as FLAC and Shorten, are not natively supported by
Apple's iTunes software. So, users of iTunes software who want to use a lossless format have to use ALAC
[8]. Apple Lossless supports bit depths of 16, 20, 24 and 32 bits and any arbitrary integer sample rate from
1 to 384,000 Hz.

Int J Elec & Comp Eng ISSN: 2088-8708 
Optimizing Apple Lossless Audio Codec algorithm using NVIDIA CUDA architecture (Rafid Ahmed)
71
In comparison with the traditional GPGPU techniques, CUDA offers several advantages, such as
shared memory, faster downloads, fully support for integer and bitwise operations and also CUDA code is
easily understandable than OpenCL [9]. The process flow of CUDA programming is described below [10]:
a. Data is copied from the main memory to the GPU global memory
b. CPU sends the processing instructions to the GPU
c. Each core at the GPU memory parallelly executes the data
d. Result from GPU global memory is copied back to main memory
In this paper, we propose an implementation of ALAC audio compression on NVIDIA GPUs.
ALAC algorithm follows the same basic principle of other lossless audio compression illustrated in
Figure 1 [11]. It is a highly serialized algorithm which is not efficient enough to be used on GPU. For
example, as the input audio file is separated into packets, the encoding of next packet depends on the results
of previous encoded packet. Our redesigned implementation for the CUDA framework aims to parallelly
implement the parts of the algorithm where the computation of a method is not serialized by previous
computations.
The paper is presented as follows. Section 2 describes the implementation of CUDA model on
ALAC Encoding and Decoding process. The experimental result with hardware and software setups are
discussed in section 3 and finally conclusions will be made in section 4.
2. RESEARCH METHOD
2.1. SIMT Model
The GPU core contains a number of Streaming Multiprocessors (SMs). On each SM, execution of
each instruction follows a model like SIMD which is called SIMT referred by Nvidia. In SIMT, the same
instruction is assigned to all the threads in the chosen warp. All threads in a warp are issued the same
instruction, although not every thread needs to execute that instruction. As a result, threads in a warp
diverging across different paths in a branch results in a loss of parallelism [12]. In our implementation, the
branching factor is handled in CPU before calling the kernel to gain total parallelism. The kernel block size
must be chosen less than or equal to tile size such that one or more elements of a tile is loaded into shared
memory by each thread in a block. Resultantly, the device performs one instruction fetch for a block of
threads which is in SIMT manner. This shortens instruction fetch and processing overhead of load instruction
[13]. In the tests, we determine that 512 threads per block configuration is giving the best performance. In the
CUDA implementation of ALAC algorithm, we have decided to exploit the framing and mixing phase of
encoding. The decoding of an ALAC audio to pcm (pulse code modulation) data are done by following the
steps of Figure 1 reversely. So, for the decoding section, we attempted to parallelize the un-mixing and
concatenating phase.
Figure 1. The basic operation of ALAC encoder
2.1.1. SIMT Model in Encoding
In serial CPU implementation of ALAC, the input data is divided to several frames by the encoding
phase, where a frame is split into even smaller pieces to carry out mixing operation. As shown in Figure 2,
the possible way to utilize this serialization of framing and mixing of encoding a stereo audio is to batch all
the input packets into CUDA global memory for a single parallel operation of mixing the data as shown in
Figure 3 taking advantage of SIMT nature.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 1, February 2018 : 70 – 75
72
Figure 2. CPU implementation of Framing and Mixing
phase
Figure 3. GPU implementation of Framing and
Mixing phase
2.1.2. SIMT Model in Decoding
The decoding process is reverse of the encoding process. First ALAC audio data is decompressed.
Then the predictors are run over the data to convert it to pcm data. Finally, for stereo audio, un-mix function
is carried out to concatenate the 2 channels into a single output buffer. As the same independent behavior
exist in the decoding process to make use of the data parallelism in CUDA, we distribute the work of the end
of the decoding process across the GPU.
2.2. Memory Coalescing
According to Jang B, Schaa D, et al, GPU memory subsystems are designed to deliver high
bandwidth versus low-latency access. To gain highest throughput, a large number of small memory accesses
should be buffered, reordered, and coalesced into a small number of large requests [14].
For strided global memory access, the effective bandwidth is always poor. When concurrent threads
simultaneously access memory addresses that are located far apart in physical memory, there is no possibility
for coalescing the memory access [15]. Thus, we restructure the buffer in such a way that global memory
loads issued by threads of warp are coalesced in encoding process as show in Figure 4.
Figure 2. Uncoalesced and coalesced memory access
2.3. Pinned Memory
By default, CPU data allocations are pageable. As CUDA driver’s uses page-locked or pinned
memory, GPU cannot directly access data prom pageable host memory. Consequently, the GPU first
allocates a temporary pinned memory, copies host data into it and then transfers data to the device memory.
So, it is better to use pinned memory beforehand rather than using paged memory in order to reduce copying
cost [16]. As we have to transfer channel buffers from GPU to CPU for each iteration while encoding frames,
we kept the channel buffer of CPU as pinned memory for fast transfer of data.
3. RESULTS AND ANALYSIS
To analyze the performance of CUDA implementation, we used a GeForce GTX 960 card with
CUDA version 7.5 installed on a machine with Intel(R) Core(TM) i5-4590 CPU running at 3.30GHz. The

73
CPU implementation of ALAC is also tested on the same testbed. To compare the performance of our GPU
implementation of ALAC algorithm, we selected representative of five audio files for testing. All these files
have the CD format, i.e. 44.1kHz, 16 bits.
a. Track 5 of [17] (28 s): Electronic Gong 5kHz (Mono)
b. Track 20 of [17] (39 s): Saxophone
c. Track 50 of [17] (22 s): Male speech (English)
d. Track 68 of [17] (2 min 44 s): Orchestra
e. Track 70 of [17] (21 s): Song by Eddie Rabbitt
The audio files are collected from Sound Quality Assessment Material (SQAM) recordings for subjective
tests 9. The flies were converted to WAV from FLAC to work with pcm data. To measure the results, we ran
our test 10 times on each dataset for each reading and showed the average running results in this section.
3.1. Encoding Results
3.1.1. Execution Speed Gain
The results on Table 1 shows the average running speed results of both mono and stereo audio
(Track 68) for CPU and GPU separately. For 16bit audio we achieve speed gain about 67% for mono and
85% for stereo audio type. For 24bit audio the speed up is around 90% for both audio types. For 32bit audio,
the speed increase is around 81%. Here we can infer that 24bit audio conversion results in faster encoding
speed for GPU where for 16bit audio it is slower than the others.
In Figure 5, we see for first test file (Track 5) we achieve 3x speed up, for second and third test file (Track 20
& 50) we achieve 7x speed up, for fourth test file (Track 68) we gain 6.5x speed and lastly for fifth file
(Track 70) we get 5x speed up.
Table 1. Mixing/Converting phase execution time comparison for encoding process
Bit Depth
CPU execution time for
mono (ms)
GPU execution time for
mono (ms)
stereo (ms)
stereo (ms)
16 2.85 0.94 20.3 3.0259
24 25.68 1.62 63.5 5.246
32 9.32 1.71 38 4.8266
Figure 3. Mixing/Converting phase speed up for encoding and decoding process against CPU using dataset

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 1, February 2018 : 70 – 75
74
3.1.2. Power Consumption Saving
Looking at Table 2, the power consumption saving is least for 16bit audio for both mono and stereo
audio files where for 24bit audio power saving is the highest.
Table 2. CPU power saving in encoding process
Bit Depth
Power consumption saving for mono
(Watt)
Power consumption saving for stereo
(Watt)
16 0.66792 3.636927
24 5.49444 9.209082
32 2.0544 7.257163
3.2. Decoding Results
3.2.1. Execution Speed Gain
Decoding results for both mono and stereo file (Track 68) are shown in Table 3. According to the
results, we get around 85% speed increase for 16bit mono audio and 90% for 16bit stereo. For 24bit audio,
we achieve more than 90% speed up. Lastly for 32bit audio, around 89% speed up is gained. From this table,
we can also state that 24bit audio decoding is faster in GPU than the others where 16bit audio conversion is
slowest.
Table 3. Un-mixing/Converting phase execution time comparison for decoding process
Bit Depth
mono (ms)
mono (ms)
stereo (ms)
stereo (ms)
16 4.02 0.5892 10.25 1.0764
24 10.884 0.8352 28.032 1.8192
32 7.093 0.864 20.354 1.715
3.3.2. Power Consumption Saving
For decoding stage, we get less power saving than that of encoding stage. Here also 16 bit audio has
the least power saving where 24 bit audio has the highest shown in Table 4.
Table 4. CPU power saving in decoding process
Bit Depth
Power consumption saving for mono
(Watt)
Power consumption saving for stereo
(Watt)
16 0.690414 1.577814
24 1.85396 4.379118
32 1.124886 3.087194
4. CONCLUSION
In this paper, we analyzed the feasibility to use CUDA framework for Apple Lossless Audio Codec
compression algorithm. Our primary focus was on outperforming the mixing/un-mixing speed of the CPU
based ALAC implementation by using NVIDIA GPUs without losing any compression ratio. We tested our
implementation on several datasets and made comparison. Experimental results demonstrate that we achieve
average of 80-95% speed up for mixing/un-mixing audio data.

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