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optimize NVIDIA autosuggestion

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- avoid creation of a config with zero threads or blocks
- WINDOWS: reduce the used memory for the auto suggestion by the amount of already used memory
This commit is contained in:
psychocrypt
2017-11-03 21:46:27 +01:00
parent 11f1028782
commit 5acec3ea8e
2 changed files with 30 additions and 9 deletions
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xmrstak/backend/nvidia/autoAdjust.hpp
+10 -9
View File
@@ -88,17 +88,18 @@ private:
constexpr size_t byte2mib = 1024u * 1024u;
std::string conf;
int i = 0;
for(auto& ctx : nvidCtxVec)
{
conf += std::string(" // gpu: ") + ctx.name + " architecture: " + std::to_string(ctx.device_arch[0] * 10 + ctx.device_arch[1]) + "\n";
conf += std::string(" // memory: ") + std::to_string(ctx.free_device_memory / byte2mib) + "/" + std::to_string(ctx.total_device_memory / byte2mib) + " MiB\n";
conf += std::string(" { \"index\" : ") + std::to_string(ctx.device_id) + ",\n" +
" \"threads\" : " + std::to_string(ctx.device_threads) + ", \"blocks\" : " + std::to_string(ctx.device_blocks) + ",\n" +
" \"bfactor\" : " + std::to_string(ctx.device_bfactor) + ", \"bsleep\" : " + std::to_string(ctx.device_bsleep) + ",\n" +
" \"affine_to_cpu\" : false,\n" +
" },\n";
++i;
if(ctx.device_threads * ctx.device_blocks > 0)
{
conf += std::string(" // gpu: ") + ctx.name + " architecture: " + std::to_string(ctx.device_arch[0] * 10 + ctx.device_arch[1]) + "\n";
conf += std::string(" // memory: ") + std::to_string(ctx.free_device_memory / byte2mib) + "/" + std::to_string(ctx.total_device_memory / byte2mib) + " MiB\n";
conf += std::string(" { \"index\" : ") + std::to_string(ctx.device_id) + ",\n" +
" \"threads\" : " + std::to_string(ctx.device_threads) + ", \"blocks\" : " + std::to_string(ctx.device_blocks) + ",\n" +
" \"bfactor\" : " + std::to_string(ctx.device_bfactor) + ", \"bsleep\" : " + std::to_string(ctx.device_bsleep) + ",\n" +
" \"affine_to_cpu\" : false,\n" +
" },\n";
}
}
configTpl.replace("GPUCONFIG",conf);
xmrstak/backend/nvidia/nvcc_code/cuda_extra.cu
+20
View File
@@ -364,6 +364,26 @@ extern "C" int cuda_get_deviceinfo(nvid_ctx* ctx)
hashMemSize = AEON_MEMORY;
}
#ifdef WIN32
/* We use in windows bfactor (split slow kernel into smaller parts) to avoid
* that windows is killing long running kernel.
* In the case there is already memory used on the gpu than we
* assume that other application are running between the split kernel,
* this can result into TLB memory flushes and can strongly reduce the performance
* and the result can be that windows is killing the miner.
* Be reducing maxMemUsage we try to avoid this effect.
*/
size_t usedMem = totalMemory - freeMemory;
if(usedMem >= maxMemUsage)
{
printf("WARNING: NVIDIA GPU %d: already %s MiB memory in use, skip GPU.\n",
ctx->device_id,
std::to_string(usedMem/byteToMiB).c_str());
}
else
maxMemUsage -= usedMem;
#endif
// keep 128MiB memory free (value is randomly chosen)
// 200byte are meta data memory (result nonce, ...)
size_t availableMem = freeMemory - (128u * byteToMiB) - 200u;