+ advances in the parallelization of music and audio applications eric battenberg, david wessel...
TRANSCRIPT
+Advances in the Parallelization
of Music and Audio Applications
Eric Battenberg, David Wessel
& Juan Colmenares
+Overview
Parallelism today in the popular interactive music languages
Parallel Partitioned Convolution
Accelerating Non-Negative Matrix Factorization (NMF) for use in audio source separation and music information retrieval and the importance of Selective, Embedded Just In Time Specialization (SEJITS)
Real-time in the Tessellation OS
A plea for more flexible I/0 with GPUs
+Current Support for Parallelism is Copy-Based
The widely used languages for music and audio applications are fundamentally sequential in character – this includes Max/MSP, PD, SuperCollider, and CHUCK among others.
Limited multithreading One approach to exploiting multi-core processors is to run
copies of the applications on separate cores. Max/MSP provides a useful multi-threading mechanism
called poly~ . PD provides PD~ each instance of which runs in a separate
thread inside a PD patch.
+Partitioned Convolution
First real-time app in the Par Lab.
Partitioned Convolution – an efficient way to do low-latency filtering with a long (> 1 sec) impulse response.
Important in real-time reverb processing for environment simulation.
Sound examples:
Acoustic Guitar …in a giant mausoleum …convolved with a sine sweep
Impulse response Impulse response
+Partitioned Convolution
Convolution: a way to do linear filtering with a finite impulse response (FIR) filter.
Direct convolution: For length L filter, O(L) ops per output point, zero delay. L can be greater than 100,000 samples (> 3 sec of audio)
Block FFT Convolution: Only O(log(L)) ops per output point, but delay of L.
How can we trade off between complexity and latency?
y[n] h[k]x[n k]k
FFT Complex Mult IFFTx y
H H = FFT(h)
+Uniform Partitioned Convolution
We would like the latency to be less than 10ms (512 samples)
Cut an impulse response up into equal-sized blocks.
Then we can use a parallellayout of Block FFT convolverswith delays to implement the filter.
The latency is now N, and we still get complexity savings.
LN
1 43 52
delay(N)
delay(N)
delay(N)
delay(N)
1
2
3
4
5
+
x
y
Block FFT Convolver
+Frequency Delay Line Convolution We can also exploit linearity of the FFT so that only one
FFT/IFFT is required.
So the parallel Block FFT Convolver above becomes a Frequency Delay Line (FDL) Convolver:
delay(N)
delay(N)
1
2
3
+
x
y
FFT
Complex Mult IFFT
H1
Block FFT Convolver
delay(N)
delay(N)
+
x
y
Complex Mult
H1
Complex Mult
H2
Complex Mult
H3
FFT
IFFT
Frequency Delay Line Convolver
+Multiple FDL Convolution
If L is big (e.g. > 100,000) and N is small (e.g. < 1000), our FDL will have 100’s of partitions to handle.
We can connect multiple FDL’s in parallel to get the best of both worlds.
xdelay(Nx
6)
delay(4Nx4)
FDL 1
FDL 2
FDL 3
+y
x FDL y
+Scheduling Multiple FDLs
FDLs are run in separate threads. Each is allowed to compute for a length of time
corresponding to its block size. Synchronization is performed at the vertical lines.
+Auto-Tuning for Real-Time
We are not trying to only maximize throughput.
We are trying to improve our ability to make real-time guarantees.
For now, we estimate a Worst-Case Execution Time (WCET) for each size of FDL. Then we combine the FDLs that are most likely to meet
their scheduling deadlines.
In the future, we will use a notion of predictability along with more robust scheduling.
We are finishing development on a Max/MSP object, Audio Unit plugin, and a portable standalone version of this.
+ Accelerating Non-Negative Matrix Factorization (NMF)
NMF is widely used in audio source separation. The idea is to factor the time/frequency representation (spectogram) into source coupled spectral (W) and gain (H) matricies.
+The Importance of SEJITS
in Developing an Information Retrieval (MIR) Application
Rather using a domain restricted language developers write in a full blown scripting language such as PYTHON or RUBY.
Functions are selected by annotation as performance critical.
If efficiency layer implementations of these functions are available appropriate code is generated and JIT compiled.
If not the selected function is executed in the scripting language itself.
The scripted implementation remains as the portable reference implementation.
With this simple music computer application we expect to initially show that Tessellation can provide acceptable performance and time predictability
In cooperation with the OS Group
2nd-level RT scheduler A
Cell A
2nd-level RT scheduler B
Cell BInitial Cell
Sound card
Shell
F
Output
Input
Music Program
End-to-end Deadline
Intermediate Deadline
Audio Processing & Synthesis Engine
Channel
F
Most of the engine’s
functionality
FilterParallel version of a partition-based convolution algorithm
Audio Input
Additional Cells
A real-time application in Tessellation
2nd-level Scheduling
Cell
Tessellation Kernel(Partition Support)
(*) Bottom part of the diagram was adapted from Liu and Asanovic, “Mitosys: ParLab Manycore OS Architecture,” Jan. 2008.
1.A) Cell and Space Partitioning
A Spatial Partition (or Cell) comprises a group of processors acting within a hardware boundary
Each cell receives a vector of basic resources– Some number of processors, a portion of
physical memory, a portion of shared cache memory, and potentially a fraction of memory bandwidth
A cell may also receive – Exclusive access to other resources
(e.g., certain hardware devices and raw storage partition)
– Guaranteed fractional services (i.e., QoS guarantees) from other partitions (e.g., network service and file service)
CCPPUULL11
L2L2BaBanknk
DRDRAMAM
DRAM & I/O InterconnectDRAM & I/O Interconnect
L1 InterconnectL1 Interconnect
CCPPUULL11
L2L2BaBanknk
DRDRAMAM
CCPPUULL11
L2L2BaBanknk
DRDRAMAM
CCPPUULL11
L2L2BaBanknk
DRDRAMAM
CCPPUULL11
L2L2BaBanknk
DRDRAMAM
CCPPUULL11
L2L2BaBanknk
DRDRAMAM
(+) Fraction of memory bandwidth
+
Time-sensitive Network
Subsystem
Time-sensitive Network
Subsystem
Network Service
(Net Partition)
Network Service
(Net Partition)
Input device(Pinned/TT Partition)
Input device(Pinned/TT Partition)
Graphical Interface
(GUI Partition)
Graphical Interface
(GUI Partition)
Audio-processing / Synthesis Engine(Pinned/TT partition)
Audio-processing / Synthesis Engine(Pinned/TT partition)
Output device(Pinned/TT Partition)
Output device(Pinned/TT Partition)
GUI SubsystemGUI Subsystem
Communication with other audio-processing nodes
Music program
Preliminary
Example of Music Application
+
A plea for more flexible GPU I/O
+
Thanks for your attention.
+Reserve Slides
+
Tessellation OSTessellation: 19
November 12t
h, 200
9
Tessellation in Server Environment
DiskDiskI/OI/O
DriversDrivers
OtherOtherDevicesDevices
NetworkNetworkQoSQoS
MonitorMonitorAndAnd
AdaptAdapt
Persistent Storage &Persistent Storage &Parallel File SystemParallel File System
Large Compute-BoundLarge Compute-BoundApplicationApplication
Large I/O-BoundLarge I/O-BoundApplicationApplication
DiskDiskI/OI/O
DriversDrivers
OtherOtherDevicesDevices
NetworkNetworkQoSQoS
MonitorMonitorAndAnd
AdaptAdapt
Persistent Storage &Persistent Storage &Parallel File SystemParallel File System
Large Compute-BoundLarge Compute-BoundApplicationApplication
Large I/O-BoundLarge I/O-BoundApplicationApplication
DiskDiskI/OI/O
DriversDrivers
OtherOtherDevicesDevices
NetworkNetworkQoSQoS
MonitorMonitorAndAnd
AdaptAdapt
Persistent Storage &Persistent Storage &Parallel File SystemParallel File System
Large Compute-BoundLarge Compute-BoundApplicationApplication
Large I/O-BoundLarge I/O-BoundApplicationApplication
DiskDiskI/OI/O
DriversDrivers
OtherOtherDevicesDevices
NetworkNetworkQoSQoS
MonitorMonitorAndAnd
AdaptAdapt
Persistent Storage &Persistent Storage &Parallel File SystemParallel File System
Large Compute-BoundLarge Compute-BoundApplicationApplication
Large I/O-BoundLarge I/O-BoundApplicationApplication
QoS
QoS
Guarantees
Guarantees
Cloud Cloud StorageStorageBW QoSBW QoS
QoSQoS
Guarantees
Guarantees
QoSQoSGuaranteesGuarantees
QoS
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