ScalaCL lets you run Scala code on GPUs through OpenCL (BSD-licensed).

WORK IN PROGRESS (see ScalaCL if you want something that works, albeit only on Scala 2.9.x).

See slides from ScalaCL + Reified talk @ scala.io 2013.

Features of the new design (v3, rewritten from scratch again!):

• Much better asynchronicity support (now requires OpenCL 1.1), and much better performance in general
• Support for captures of constants and OpenCL arrays
• Support for lazy clones for fast zipping
• Kernels are now fully specialized on static types and generated at compile-time (allows much faster startup and caching at runtime)
• ScalaCL Collections no longer fit in regular Scala Collections, to avoid silent data transfers / conversions when using unaccelerated methods (syntax stays the same, though)
• No more CLRange: expecting compiler to do its job

TODO

• Finish Scalaxy/Reified integration (started under CLFunc / CLFuncUtils)
• Add more tests: DataIO, CodeConversion, scheduling, uniqueness / caching of kernels
• Implement more DataIO[T], support case classes as tuples
• Catch up with compiler plugin:
  • Auto-vectorization
    • 1D works
    • Add 2D
    • add filters
  • Import Scalaxy streams, make them work with scala.reflection.api.Universe
• Plug some v2 runtime code back (filtered array compaction, reduceSymmetric, parallel sums...)
• Benchmarks!
• Wanna help? Ping the NativeLibs4Java mailing-list!

Usage

scalaVersion := "2.11.4"

libraryDependencies += "com.nativelibs4java" %% "scalacl" % "0.3-SNAPSHOT"

// Avoid sbt-related macro classpath issues.
fork := true

// Scalaxy/Reified snapshots are published on the Sonatype repository.
resolvers += Resolver.sonatypeRepo("snapshots")

Examples

The following example currently works:

import scalacl._

case class Matrix(data: CLArray[Float],
                  rows: Int,
                  columns: Int)
                 (implicit context: Context)
{
  def this(rows: Int, columns: Int)
          (implicit context: Context) =
    this(new CLArray[Float](rows * columns), rows, columns)

  def this(n: Int)
          (implicit context: Context) =
    this(n, n)

  def putProduct(a: Matrix, b: Matrix): Unit = {
    assert(a.columns == b.rows)
    assert(a.rows == rows)
    assert(b.columns == columns)
    
    kernel {
      // This block will either be converted to an OpenCL kernel or cause compilation error
      for (i <- 0 until rows;
           j <- 0 until columns) {
        // c(i, j) = sum(k, a(i, k) * b(k, j))
        data(i * columns + j) = (
          for (k <- 0 until a.columns) yield
            a.data(i * a.columns + k) * b.data(k * b.columns + j)
        ).sum
      }
    }
  }
  
  def putSum(a: Matrix, b: Matrix): Unit = {
    assert(a.columns == b.columns && a.columns == columns)
    assert(a.rows == b.rows && a.rows == rows)
    
    kernel {
      for (i <- 0 until rows; j <- 0 until columns) {
      	val offset = i * columns + j
        data(offset) = a.data(offset) + b.data(offset)
      }
    }
  }
}

implicit val context = Context.best

val n = 10
val a = new Matrix(n)
val b = new Matrix(n)
val out = new Matrix(n)

out.putProduct(a, b)

println(out.data)