SIMD Intrinsics in the JVM
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.

Artifact description

Submission and reviewing guidelines and methodology:


To reproduce the results presented in our work, we provide an artifact that consist of two parts:

  • lms-intrinsics a precompiled jar library that includes all Intel-based SIMD intrinsics functions, implemented as Scala eDSLs in LMS.

  • NGen runtime implemented in Scala and Java, that enables the use of lms-intrinsics in the JVM and includes the experiments discussed in our work.

The SIMD based eDSLs follow the modular design of the LMS framework and are implemented as an external LMS library, separated from the JVM runtime. This allows a stand-alone use of lms-intrinsics, enabling LMS to generate x86 vectorized code outside the context of the JVM. The JVM runtime (NGen) demonstrates the use of the lms-intrinsics by providing the compiler pipeline to generate, compile, link and execute the LMS-generated SIMD code and has a strong dependency on this library.

The experiments included in the artifact come in the form of microbenchmarks. While the most convenient deployment for this artifact would have been a Docker image through Collective Knowledge, we decided to eliminate the overhead imposed by the containers and provided a bare metal deployment that aims at providing as precise results possible for our tests. To achieve that, we use SBT (Simple Build Tool) to build and execute our experiments.


Check-list (artifact meta information)

  • Algorithm: Using SIMD intrinsics in the JVM. Experiments include dot-product on quantized arrays, BLAS routines: SAXPY and Matrix-Matrix-Multiplication.
  • Compilation: lms-intrinsics is a precompiled library, compiled with Scala 2.11 and is available as a jar bundle, accessible through Maven. NGen requires Scala 2.11 and Java 1.8 for compilation. Both NGen and lms-intrinsics generate C code that is compiled with GCC, ICC or LLVM.
  • Transformations: To make SIMD instructions available in the JVM, NGen uses LMS as a staging framework. The user writes vectorized code as eDSL in Scala and NGen stages the code through multiple compile phases before execution.
  • Binary: lms-intrinsics is a jar bundle. NGen includes binaries for SBT v0.13.6, as well as small library for CPUID inspection and Sigar v1.6.5_01 (System Information Gatherer And Reporter binaries. NGen has various dependencies on precompiled libraries that include BridJ, Apache Commons, ScalaMeter, Scala Virtualized, LMS and finally lms-intrinsics. SBT automatically pulls all dependencies and their corresponding versions.
  • Data set: Our experiments operate with random data, requiring no data set.
  • Run-time environment: lms-intrinsics can run on any JVM that supports LMS and any operating system supported by the same JVM. Similarly, NGen could work in any JVM that supports LMS, reflection and native code invocation, however our focus has been on the HotSpot JVM only, supporting Windows, Linux and Mac OS X. Our results are most conveniently replicated on a Unix environment.
  • Hardware: The NGen and lms-intrinsics generated code can run on any x86 and x86-64 architecture that supports at least one subset of the Intel intrinsics functions. We recommend a Haswell machine for verifying the results presented in the paper to obtain comparable results.
  • Run-time state: We perform our tests using warm cache scenario, warming the code and data cache many times before measurements begin. We advise that the replication of our experiments to be done with minimal interference of other applications running on the system, having technologies for frequency scaling and resource sharing disabled.
  • Output: NGen generates performance profile of each algorithm presented in this paper.
  • Experiment workflow: We use SBT not only to compile the code, but also to run the experiments.
  • Experiment customization: Customisation is certainly possible and can be easily achieved by implementing any vectorized code as a Scala eDSL.
  • Publicly available: Yes

How delivered

The precompiled SIMD eDSLs library, as well as our JVM runtime, including the supporting experiments are publicly available through GitHub, on the following links:

Note that lms-intrinsics is also available through Maven, and can be used through SBT directly:

libraryDependencies += "ch.ethz.acl" %% "lms-intrinsics" % "0.0.3-SNAPSHOT"

Hardware dependencies

lms-intrinsics as well as NGen are able to generate C code that can run on x86 and x86-64 architecture supporting Intel ISAs. However, the full set of our experiments require at least a Haswell machine. Namely:

  • SAXPY and MMM algorithms are implemented using AVX and FMA ISAs, and therefore require at least a Haswell enabled process. Broadwell, Skylake, Kaby Lake or later would also work.
  • The dot product of the quantized arrays relies on AVX2, and FMA flags, but also uses the hardware random number generator, requiring the RDRAND ISA, as well FP16C to deal with half-precision floats.

We recommend disabling Intel Turbo Boost and Hyper-Threading technologies to avoid the effects of frequency scaling and resource sharing on the measurements. Note that these technologies can be easily disabled in the BIOS settings of the machines that have BIOS firmware. Many Apple-based machines, such as the MacBook or others, do not have a user accessible BIOS firmware, and could only disable Turbo Boost using external kernel modules such as Turbo Boost Switcher (

Software dependencies

lms-intrinsics is a self-contained precompiled library and all of its software dependencies are handled automatically through Maven tools such as SBT. To build and run NGen, the following dependencies must be met:

  • Git client, used by SBT to resolve dependencies.
  • Java Development Kit (JDK) 1.8 or later.
  • C compiler such as GCC, ICC or LLVM.

After installing the dependencies, it is quite important to have the binary executables available in the $PATH. This way the SBT tool will be able to process all compilation phases as well as to execute the experiments. Make sure that the following commands work on your terminal:

git --version 
gcc --version 
java -version 
javac -version

It is also important to ensure that the installed JVM has architecture that GCC can compile to. This is particularly important for Windows users: 32-bit MinGW port of GCC will fail to compile code for 64-bit JVM.


The artifact can be cloned from the GitHub repository:

git clone

The artifact already includes a precompiled version of SBT. Therefore, to start the SBT console, we run:

cd ngen

# For Unix users:

# For Windows users

Once started, we can compile the code using:

> compile

Once invoked, SBT will automatically pull lms-intrinsics as well as all other dependencies and start the compilation.

Experiment workflow

Once SBT compiles the code, we can proceed with evaluating our experiments. We do this through the SBT console. To inspect the testing machine through NGen runtime we use:

> test-only cgo.TestPlatform

The runtime will be able to inspect the CPU, identify available ISAs and compilers and inspect the current JDK. If the test platform is successfully identified, we can continue with the experiments.

Generating SIMD eDSLs.

The lms-intrinsics bundle includes the automatic generator of SIMD eDSLs, invoked by:

> test-only cgo.GenerateIntrinsics

The Scala eDSLs (coupled with statistics) will be generated in Generated_SIMD_Intrinsics folder.

Explicit vectorization in the JVM.

To run the experiments depicted in our work, we use:

> test-only cgo.TestSaxpy
> test-only cgo.TestMMM
> test-only cgo.TestPrecision

In the case of SAXPY algorithm, if the testing machine is not Haswell based, we provided an architecture independent implementation of SAXPY:

> test-only cgo.TestMultiSaxpy

Each result shows the size of our microbenchmarks, and the obtained performance in flops/cycle.

Evaluation and expected result

In the evaluation of the experiment workflow, we expect LMS to produce correct vectorized code using lms-intrinsics. Furthermore, we expect our performance results to depict a consistent behaviour to the results shown in this work, outperforming the JVM on the microarchitectures that support our experiments. Finally, we expect the automatic generation of eDSLs to be easily adjustable to subsequent updates on the Intel Intrinsics specifications.

Experiment customization

There are many opportunities for customization. We can use NGen to easily develop vectorized code, and we can use ScalaMeter to adjust the current benchmarks.

Developing SIMD code.

NSaxpy.scala class, available in src/ch/ethz/acl/ngen/saxpy/, provides detailed guidelines for the usage of SIMD in Scala. Following the comments in the file, as well as the structural flow of the program, one can easily modify the skeleton to perform other type of vectorized computations.

Customizing Benchmarks.

Each performance experiment, uses ScalaMeter and is implemented as a Scala class. The Matrix-Matrix-Multiplication includes BenchMMM.scala located in src/ch/ethz/acl/ngen/mmm/. The implementaton allows changes to various aspects of the benchmarks, including the size and the values of the input data, warm up times, different JVM invocations, etc.