GPU¶
Usage
use GPU;
or
import GPU;
Supports utility functions for operating with GPUs.
Warning
This module is unstable and its interface is subject to change in the future.
GPU support is a relatively new feature to Chapel and is under active development.
For the most up-to-date information about GPU support see the technical note about it.
- proc gpuWrite(const args ...?k)¶
This function is intended to be called from within a GPU kernel and is useful for debugging purposes.
Currently using
write
to send output tostdout
will make a loop ineligible for GPU execution; usegpuWrite
instead.Currently this function will only work if values of type
c_ptrConst(c_char)
are passed.On NVIDIA GPUs the written values will be flushed to the terminal after the kernel has finished executing. Note that there is a 1MB limit on the size of this buffer.
- proc assertOnGpu()¶
Warning
the functional form of assertOnGpu() is deprecated. Please use the @assertOnGpu loop attribute instead.
Will halt execution at runtime if called from outside a GPU. If used on first line in
foreach
orforall
loop will also do a compile time check that the loop is eligible for execution on a GPU.
- proc gpuClock() : uint¶
Returns value of a per-multiprocessor counter that increments every clock cycle. This function is meant to be called to time sections of code within a GPU enabled loop.
- proc gpuClocksPerSec(devNum: int)¶
Returns the number of clock cycles per second of a GPU multiprocessor. Note: currently we don’t support calling this function from within a kernel.
- proc syncThreads()¶
Synchronize threads within a GPU block.
- proc syncWarp(mask: uint(32) = 0xffffffff)¶
Causes the executing thread to wait until all warp lanes named in mask have executed a
syncWarp()
(with the same mask) before resuming execution. Each calling thread must have its own bit set in the mask and all non-exited threads named in mask must execute a correspondingsyncWarp()
with the same mask, or the result is undefined.
Allocate block shared memory, enough to store
size
elements ofeltType
. Returns aCTypes.c_ptr
to the allocated array. Note that although every thread in a block calls this procedure, the same shared array is returned to all of them.- Arguments:
eltType – the type of elements to allocate the array for.
size – the number of elements in each GPU thread block’s copy of the array.
- proc setBlockSize(blockSize: integral)¶
Warning
the functional form of setBlockSize(size) is deprecated. Please use the @gpu.blockSize(size) loop attribute instead.
Set the block size for kernels launched on the GPU.
- proc gpuAtomicAdd(ref x: ?T, val: T) : T¶
When run on a GPU, atomically add ‘val’ to ‘x’ and store the result in ‘x’. The operation returns the old value of x.
- proc gpuAtomicSub(ref x: ?T, val: T) : T¶
When run on a GPU, atomically subtract ‘val’ from ‘x’ and store the result in ‘x’. The operation returns the old value of x.
- proc gpuAtomicExch(ref x: ?T, val: T) : T¶
When run on a GPU, atomically exchange the value stored in ‘x’ with ‘val’. The operation returns the old value of x.
- proc gpuAtomicMin(ref x: ?T, val: T) : T¶
When run on a GPU, atomically compare ‘x’ and ‘val’ and store the minimum in ‘x’. The operation returns the old value of x.
- proc gpuAtomicMax(ref x: ?T, val: T) : T¶
When run on a GPU, atomically compare ‘x’ and ‘val’ and store the maximum in ‘x’. The operation returns the old value of x.
- proc gpuAtomicInc(ref x: ?T, val: T) : T¶
When run on a GPU, atomically increments x if the original value of x is greater-than or equal to val, if so the result is stored in ‘x’. Otherwise x is set to 0. The operation returns the old value of x.
- proc gpuAtomicDec(ref x: ?T, val: T) : T¶
When run on a GPU, atomically determine if ‘x’ equals 0 or is greater than ‘val’. If so store ‘val’ in ‘x’ otherwise decrement ‘x’ by 1. Otherwise x is set to val. The operation returns the old value of x.
- proc gpuAtomicAnd(ref x: ?T, val: T) : T¶
When run on a GPU, atomically perform a bitwise ‘and’ operation on ‘x’ and ‘val’ and store the result in ‘x’. The operation returns the old value of x.
- proc gpuAtomicOr(ref x: ?T, val: T) : T¶
When run on a GPU, atomically perform a bitwise ‘or’ operation on ‘x’ and ‘val’ and store the result in ‘x’. The operation returns the old value of x.
- proc gpuAtomicXor(ref x: ?T, val: T) : T¶
When run on a GPU, atomically perform a bitwise ‘xor’ operation on ‘x’ and ‘val’ and store the result in ‘x’. The operation returns the old value of x.
- proc gpuAtomicCAS(ref x: ?T, cmp: T, val: T) : T¶
When run on a GPU, atomically compare the value in ‘x’ and ‘cmp’, if they are equal store ‘val’ in ‘x’. The operation returns the old value of x.
- proc gpuSumReduce(const ref A: [] ?t)¶
Add all elements of an array together on the GPU (that is, perform a sum-reduction). The array must be in GPU-accessible memory and the function must be called from outside a GPU-eligible loop. Only arrays with int, uint, and real types are supported. A simple example is the following:
on here.gpus[0] { var Arr = [3, 2, 1, 5, 4]; // will be GPU-accessible writeln(gpuSumReduce(Arr)); // 15 }
- proc gpuMinReduce(const ref A: [] ?t)¶
Return the minimum element of an array on the GPU (that is, perform a min-reduction). The array must be in GPU-accessible memory and the function must be called from outside a GPU-eligible loop. Only arrays with int, uint, and real types are supported. A simple example is the following:
on here.gpus[0] { var Arr = [3, 2, 1, 5, 4]; // will be GPU-accessible writeln(gpuMinReduce(Arr)); // 1 }
- proc gpuMaxReduce(const ref A: [] ?t)¶
Return the maximum element of an array on the GPU (that is, perform a max-reduction). The array must be in GPU-accessible memory and the function must be called from outside a GPU-eligible loop. Only arrays with int, uint, and real types are supported. A simple example is the following:
on here.gpus[0] { var Arr = [3, 2, 1, 5, 4]; // will be GPU-accessible writeln(gpuMaxReduce(Arr)); // 5 }
- proc gpuMinLocReduce(const ref A: [] ?t)¶
For an array on the GPU, return a tuple with the value and the index of the minimum element (that is, perform a minloc-reduction). If there are multiple elements with the same minimum value, the index of the first one is returned. The array must be in GPU-accessible memory and the function must be called from outside a GPU-eligible loop. Only arrays with int, uint, and real types are supported. A simple example is the following:
on here.gpus[0] { var Arr = [3, 2, 1, 5, 4]; // will be GPU-accessible writeln(gpuMinLocReduce(Arr)); // (1, 2). Note that Arr[2]==1. }
- proc gpuMaxLocReduce(const ref A: [] ?t)¶
For an array on the GPU, return a tuple with the value and the index of the maximum element (that is, perform a maxloc-reduction). If there are multiple elements with the same maximum value, the index of the first one is returned. The array must be in GPU-accessible memory and the function must be called from outside a GPU-eligible loop. Only arrays with int, uint, and real types are supported. A simple example is the following:
on here.gpus[0] { var Arr = [3, 2, 1, 5, 4]; // will be GPU-accessible writeln(gpuMaxLocReduce(Arr)); // (5, 3). Note that Arr[3]==5. }
- proc gpuScan(ref gpuArr: [] ?t) where isNumericType(t) && !isComplexType(t)¶
Calculates an exclusive prefix sum (scan) of an array on the GPU. The array must be in GPU-accessible memory and the function must be called from outside a GPU-eligible loop. Arrays of numeric types are supported. A simple example is the following:
on here.gpus[0] { var Arr = [3, 2, 1, 5, 4]; // will be GPU-accessible gpuScan(Arr); writeln(Arr); // [0, 3, 5, 6, 11] }
- proc gpuSort(ref gpuInputArr: [] ?t)¶
Sort an array on the GPU. The array must be in GPU-accessible memory and the function must be called from outside a GPU-eligible loop. Only arrays of numeric types are supported. A simple example is the following:
on here.gpus[0] { var Arr = [3, 2, 1, 5, 4] : uint; // will be GPU-accessible gpuSort(Arr); writeln(Arr); // [1, 2, 3, 4, 5] }