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even-perfect-numbers-scanner/PerfectNumbers.Core/Gpu/GpuContextPool.cs
T
Marek Lemańczyk 4c4d4243fb Merge remote-tracking branch 'github/codex/optimize-evenperfectbitscanner-for-static-arrays-khd9w8' into work
# Conflicts:
#	PerfectNumbers.Core/Gpu/GpuContextPool.cs
#	PerfectNumbers.Core/Gpu/GpuKernelLease.cs
#	PerfectNumbers.Core/Gpu/GpuKernelPool.cs
#	PerfectNumbers.Core/Gpu/GpuStaticTableInitializer.cs
#	PerfectNumbers.Core/Gpu/Kernels/PrimeOrderSpecialMaxKernels.cs
#	PerfectNumbers.Core/PrimeOrderCalculator.Cpu.cs
#	PerfectNumbers.Core/PrimeOrderCalculator.Gpu.cs
#	PerfectNumbers.Core/PrimeTester.cs
2025-11-03 23:59:12 +01:00

128 lines
3.2 KiB
C#

using System.Collections.Concurrent;
using ILGPU;
using ILGPU.Runtime;
namespace PerfectNumbers.Core.Gpu;
public static class GpuContextPool
{
// The pool intentionally skips pre-loading ProcessEightBitWindows kernels so accelerator initialization
// stays stable; GpuKernelPool JIT-compiles them on first use alongside the small-cycle uploads.
// Default device preference for generic GPU kernels (prime scans, NTT, etc.)
internal sealed class PooledContext
{
public readonly Context Context;
public readonly Accelerator Accelerator;
public PooledContext(KernelContainer kernels)
{
Context = SharedGpuContext.Context;
Accelerator = SharedGpuContext.Accelerator;
GpuStaticTableInitializer.EnsureStaticTables(kernels, Accelerator);
kernels.Dispose();
// NOTE: Avoid loading/compiling any kernel here to prevent implicit
// CL stream/queue creation during accelerator construction.
// Some OpenCL drivers are fragile when a queue is created immediately
// after device init. Real kernels will be JIT-loaded at first use.
}
public void Dispose()
{
// Ensure all cached GPU buffers for this accelerator are released.
NttGpuMath.ClearCaches(Accelerator);
// Release PrimeTester GPU state for this accelerator (kernel/device primes)
// Clear any per-accelerator cached resources to avoid releasing
// after the accelerator is destroyed.
PrimeTester.ClearGpuCaches(Accelerator);
// These resources are shared between GPU leases
// Accelerator.Dispose();
// Context.Dispose();
}
}
private static readonly ConcurrentQueue<PooledContext> GpuPool = new();
private static int WarmedGpuContextCount;
// Allows callers to choose CPU/GPU per use-case, decoupled from ForceCpu.
public static GpuContextLease Rent()
{
if (GpuPool.TryDequeue(out var gpu))
{
return new GpuContextLease(gpu);
}
// Create a new accelerator when the pool does not have one available.
return new GpuContextLease(new PooledContext(GpuKernelPool.GetKernels()));
}
public static void WarmUpPool(int threadCount)
{
if (threadCount <= WarmedGpuContextCount)
{
return;
}
int toCreate = threadCount - WarmedGpuContextCount;
Console.WriteLine($"Warming up GPU context pool to {toCreate} contexts...");
var contexts = new PooledContext[toCreate];
var pool = GpuPool;
PooledContext context;
for (int i = 0; i < toCreate; i++)
{
KernelContainer kernels = GpuKernelPool.GetKernels();
context = new PooledContext(kernels);
contexts[i] = context;
pool.Enqueue(context);
}
WarmedGpuContextCount = threadCount;
}
public static void DisposeAll()
{
while (GpuPool.TryDequeue(out var gpu))
{
gpu.Dispose();
}
PrimeTester.DisposeGpuContexts();
WarmedGpuContextCount = 0;
}
private static void Return(PooledContext ctx)
{
ctx.Accelerator.Synchronize();
GpuPool.Enqueue(ctx);
}
public struct GpuContextLease
{
private PooledContext? _ctx;
private bool _disposed;
internal GpuContextLease(PooledContext ctx)
{
_ctx = ctx;
_disposed = false;
}
public Context Context => _ctx!.Context;
public Accelerator Accelerator => _ctx!.Accelerator;
public void Dispose()
{
if (_disposed)
{
return;
}
_disposed = true;
Return(_ctx!);
_ctx = null;
}
}
}