Cheatsheet

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General

Accelerator and Device

Define in-kernel thread indexing type
using Dim = DimInt<constant>;
using Idx = IntegerType;
Define accelerator type (CUDA, OpenMP,etc.)
using Acc = AcceleratorType<Dim,Idx>;
AcceleratorType:
AccGpuCudaRt,
AccCpuOmp2Blocks,
AccCpuOmp2Threads,
AccCpuOmp4,
AccCpuTbbBlocks,
AccCpuThreads,
AccCpuSerial
Select device for the given accelerator by index
auto const device = getDevByIdx<Acc>(index);

Queue and Events

Create a queue for a device
using Queue = Queue<Acc, Property>;
auto queue = Queue{device};
Property:
Blocking
NonBlocking
Put a task for execution
enqueue(queue, task);
Wait for all operations in the queue
wait(queue);
Create an event
Event<Queue> event{device};
Put an event to the queue
enqueue(queue, event);
Check if the event is completed
isComplete(event);
Wait for the event (and all operations put to the same queue before it)
wait(event);

Memory

Memory allocation and transfers are symmetric for host and devices, both done via alpaka API

Create a CPU device for memory allocation on the host side
auto const devHost = getDevByIdx<DevCpu>(0u);
Allocate a buffer in host memory
Vec<Dim, Idx> extent = value;
using BufHost = Buf<DevHost, DataType, Dim, Idx>;
BufHost bufHost = allocBuf<DataType, Idx>(devHost, extent);
(Optional, affects CPU – GPU memory copies) Prepare it for asynchronous memory copies
prepareForAsyncCopy(bufHost);
Create a view to host memory represented by a pointer
using Dim = alpaka::DimInt<1u>;
Vec<Dim, Idx> extent = value;
DataType* date = new DataType[extent[0]];
auto hostView = createView(devHost, data, extent);
Create a view to host std::vector
auto vec = std::vector<DataType>(42u);
auto hostView = createView(devHost, vec);
Create a view to host std::array
std::vector<DataType, 2> array = {42u, 23};
auto hostView = createView(devHost, array);
Get a raw pointer to a buffer or view initialization, etc.
DataType* raw = view::getPtrNative(bufHost);
DataType* rawViewPtr = view::getPtrNative(hostView);
Get an accessor to a buffer and the accessor’s type (experimental)
experimental::BufferAccessor<Acc, Elem, N, AccessTag> a = experimental::access(buffer);
Allocate a buffer in device memory
auto bufDevice = allocBuf<DataType, Idx>(device, extent);
Enqueue a memory copy from host to device
memcpy(queue, bufDevice, bufHost, extent);
Enqueue a memory copy from device to host
memcpy(queue, bufHost, bufDevice, extent);

Kernel Execution

Automatically select a valid kernel launch configuration
Vec<Dim, Idx> const globalThreadExtent = vectorValue;
Vec<Dim, Idx> const elementsPerThread = vectorValue;

auto autoWorkDiv = getValidWorkDiv<Acc>(
  device,
  globalThreadExtent, elementsPerThread,
  false,
  GridBlockExtentSubDivRestrictions::Unrestricted);
Manually set a kernel launch configuration
Vec<Dim, Idx> const blocksPerGrid = vectorValue;
Vec<Dim, Idx> const threadsPerBlock = vectorValue;
Vec<Dim, Idx> const elementsPerThread = vectorValue;

using WorkDiv = WorkDivMembers<Dim, Idx>;
auto manualWorkDiv = WorkDiv{blocksPerGrid,
                             threadsPerBlock,
                             elementsPerThread};
Instantiate a kernel and create a task that will run it (does not launch it yet)
Kernel kernel{argumentsForConstructor};
auto taskRunKernel = createTaskKernel<Acc>(workDiv, kernel, parameters);

acc parameter of the kernel is provided automatically, does not need to be specified here

Put the kernel for execution
enqueue(queue, taskRunKernel);

Kernel Implementation

Define a kernel as a C++ functor
struct Kernel {
   template<typename Acc>
   ALPAKA_FN_ACC void operator()(Acc const & acc, parameters) const { ... }
};

ALPAKA_FN_ACC is required for kernels and functions called inside, acc is mandatory first parameter, its type is the template parameter

Access multi-dimensional indices and extents of blocks, threads, and elements
auto idx = getIdx<Origin, Unit>(acc);
auto extent = getWorkDiv<Origin, Unit>(acc);
Origin:
Grid, Block, Thread
Unit:
Blocks, Threads, Elems
Access components of multi-dimensional indices and extents
auto idxX = idx[0];
Linearize multi-dimensional vectors
auto linearIdx = mapIdx<1u>(idx, extent);
Allocate static shared memory variable
Type & var = declareSharedVar<Type, __COUNTER__>(acc);
Get dynamic shared memory pool, requires the kernel to specialize
trait::BlockSharedMemDynSizeBytes
  Type * dynamicSharedMemoryPool = getDynSharedMem<Type>(acc);
Synchronize threads of the same block
syncBlockThreads(acc);
Atomic operations
auto result = atomicOp<Operation>(acc, arguments);
Operations:
AtomicAdd, AtomicSub, AtomicMin, AtomicMax, AtomicExch,
AtomicInc, AtomicDec, AtomicAnd, AtomicOr, AtomicXor, AtomicCas
Memory fences on block-, grid- or device level (guarantees LoadLoad and StoreStore ordering)
mem_fence(acc, memory_scope::Block{});
mem_fence(acc, memory_scope::Grid{});
mem_fence(acc, memory_scope::Device{});
Warp-level operations
uint64_t result = warp::ballot(acc, idx == 1 || idx == 4);
assert( result == (1<<1) + (1<<4) );

int32_t valFromSrcLane = warp::shfl(val, srcLane);
Math functions take acc as additional first argument
math::sin(acc, argument);

Similar for other math functions.

Generate random numbers
auto distribution = rand::distribution::createNormalReal<double>(acc);
auto generator = rand::engine::createDefault(acc, seed, subsequence);
auto number = distribution(generator);