design of a custom vector operation api exploiting simd intrinsics within java

Design of A Custom Vector Operation API Exploiting

SIMD Intrinsics within JavaPresented by John-Marc Desmarais

Authors: Jonathan Parri, John-Marc Desmarais, Daniel Shapiro, Miodrag Bolic and Voicu Groza

Overview Introduction What is SIMD? jSIMD Userflow Issues and Considerations Java Native Interface Current Implementation Results Future Work Conclusion

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Introduction


SIMD(Single Instruction Multiple

Data)• Many embedded systems have

begun to take advantage of the Java framework.

• JVMs can be embedded or rest on top of the OS.

• SIMD is an often under utilized option available on many processors. (O3 compilation)

• In Java it is up to the JVM to best decide how to use SIMD if available at runtime.

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SIMD


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Functional Unit

Functional Unit

Functional Unit

Functional Unit

Functional Unit

Functional Unit

…Data

Multiple processing elements that performs the same operation on data simultaneously.

Single Instruction Multiple Data

Instruction

Common SIMD Implementations

• AMD 3DNow!• Intel MMX• SSE (Streaming SIMD Extensions)

• AltiVec from Apple, IBM and Freescale

• VIS from Sun Microsystems

x86/x64

PowerPC

SPARC

xmm1xmm0

xmm3xmm2

128 bits

jSIMD: User Flow



Data)

Current SIMD Optimization Java Flow

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jSIMD SIMD Optimization Approach

Standard Java

Profile at Runtime

Change Java Code

Runtime JVM to SIMD Mapping May take very long or may not even achieve best SIMD usage

Standard Java with

jSIMD

Issues & Considerations



Data)

Packing•Packaging and aligning data into SIMD registers is very time consuming.Transactional•Intermediate values should not leave SIMD memory and register space.Target Specifics•Various targets have different SIMD implementations. (May not even exist, fallback)

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jSIMD: SIMD for Java



Data)

Java and the JNI•Java allows programs to use native libraries. •SIMD instructions can be called manually from native code.•Solution! Map all SIMD intrinsic into JNI making them invisible to the Java programmer.•No system specific code/headers are permitted in the library so compilation can be performed automatically on any platform.

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jSIMD: SIMD for Java



Data)

Current Implementation

Running Targets:Intel x86/x64AMD x86/x64SPARCPowerPC

Future Targets:NIOS II with custom SIMD Unit

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jSIMD: User Perspective



Data) • Extended Java ISA with parallel SIMD operations.

• Native operations hidden as Java methods.

• User is not concerned with native interface.

Transparency

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JavaBase Java

ISA

jSIMD API

Native SIMD

Mappings in C

Results



Data)

JVM versus Programmer Know-How

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JVM does an impressive job at SIMD mapping but is not as effective as a determined programmer with an understanding of the underlying target architecture.

Results

Design Space Exploration


Multiprocessors(Add more processors)

Memory Hierarchy

Coprocessors & Hardware

Accelerators

Multi-Core Interconnect Topology


Data)

ISE(Instruction Set Extensions)

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GPU(Graphical Processing Units)

Future Work



Data)

Future as DSE Avenue

Manual Specification and Automatic Detection for Native SIMD

Rewrite Java for jSIMD

Vectorize

Analysis

Profile

PARRI:CARG 2010

Conclusion

PARRI:CARG 2010


• VMs should integrate this approach into their languages.

• Until such time as VM support is made available, programmers can use our API to accelerate their applications.

We have shown that jSIMD can be used to accelerate VM-based applications more effectively than contemporary automated solutions.

design of a custom vector operation api exploiting simd intrinsics within java

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