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!):
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")
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)
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