Remove TODO

build.sh

@@ -1,24 +1,5 @@
 #!/bin/bash
 
-print_help() {
-  cat << EOF
-Usage: $0 <backends> [build_type]
-
-  <backends>    MANDATORY. Comma-separated list of 
-                backends to enable.
-                Valid options: hsa, intel, cuda
-                Example: hsa,cuda
-
-  [build_type]  OPTIONAL. Build type. Valid options:
-                debug, release (default: release)
-
-Examples:
-  $0 hsa,intel debug
-  $0 cuda
-  $0 hsa,intel,cuda release
-EOF
-}
-
 # gpufetch build script
 set -e
 
@@ -26,79 +7,19 @@ rm -rf build/ gpufetch
 mkdir build/
 cd build/
 
-if [ "$1" == "--help" ]
+if [ "$1" == "debug" ]
 then
-  echo "gpufetch build script"
+  BUILD_TYPE="Debug"
-  echo
-  print_help
-  exit 0
-fi
-
-if [[ $# -lt 1 ]]; then
-  echo "ERROR: At least one backend must be specified."
-  echo
-  print_help
-  exit 1
-fi
-
-# Determine if last argument is build type
-LAST_ARG="${!#}"
-if [[ "$LAST_ARG" == "debug" || "$LAST_ARG" == "release" ]]; then
-  BUILD_TYPE="$LAST_ARG"
-  BACKEND_ARG="${1}"
 else
-  BUILD_TYPE="release"
+  BUILD_TYPE="Release"
-  BACKEND_ARG="${1}"
 fi
 
-# Split comma-separated backends into an array
-IFS=',' read -r -a BACKENDS <<< "$BACKEND_ARG"
-
-# Validate build type 
-if [[ "$BUILD_TYPE" != "debug" && "$BUILD_TYPE" != "release" ]]
-then
-  echo "Error: Invalid build type '$BUILD_TYPE'."
-  echo "Valid options are: debug, release"
-  exit 1
-fi
-
-# From lower to upper case
-CMAKE_FLAGS="-DCMAKE_BUILD_TYPE=${BUILD_TYPE^}"
-
-# Validate backends
-VALID_BACKENDS=("hsa" "intel" "cuda")
-
-for BACKEND in "${BACKENDS[@]}"; do
-  case "$BACKEND" in
-    hsa)
-      CMAKE_FLAGS+=" -DENABLE_HSA_BACKEND=ON"
-      ;;
-    intel)
-      CMAKE_FLAGS+=" -DENABLE_INTEL_BACKEND=ON"
-      ;;
-    cuda)
-      CMAKE_FLAGS+=" -DENABLE_CUDA_BACKEND=ON"
-      ;;
-    *)
-      echo "ERROR: Invalid backend '$BACKEND'."
-      echo "Valid options: ${VALID_BACKENDS[*]}"
-      exit 1
-      ;;
-  esac
-done
-
-# You can also manually specify the compilation flags.
-# If you need to, just run the cmake command directly
-# instead of using this script.
-#
-# Here you will find some help:
-#
 # In case you have CUDA installed but it is not detected,
 # - set CMAKE_CUDA_COMPILER to your nvcc binary:
 # - set CMAKE_CUDA_COMPILER_TOOLKIT_ROOT to the CUDA root dir
 # for example:
 # cmake -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc -DCMAKE_CUDA_COMPILER_TOOLKIT_ROOT=/usr/local/cuda/ ..
-#
+
 # In case you want to explicitely disable a backend, you can:
 # Disable CUDA backend:
 # cmake -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DENABLE_CUDA_BACKEND=OFF ..
@@ -107,9 +28,7 @@ done
 # Disable Intel backend:
 # cmake -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DENABLE_INTEL_BACKEND=OFF ..
 
-echo "$0: Running cmake $CMAKE_FLAGS"
+cmake -DCMAKE_BUILD_TYPE=$BUILD_TYPE ..
-echo 
-cmake $CMAKE_FLAGS ..
 
 os=$(uname)
 if [ "$os" == 'Linux' ]; then

src/common/printer.cpp

@@ -72,7 +72,7 @@ static const AttributeField ATTRIBUTE_INFO[] = {
   { ATTRIBUTE_FREQUENCY,     "Max Frequency:",          "Max Freq.:" },
   { ATTRIBUTE_PEAK,          "Peak Performance:",       "Peak Perf.:" },
   { ATTRIBUTE_COMPUTE_UNITS, "Compute Units (CUs):",    "CUs" },
-  { ATTRIBUTE_MATRIX_CORES,  "Matrix Cores:",           "Matrix Cores:" },
+  { ATTRIBUTE_MATRIX_CORES,  "Matrix Cores: ",          "Matrix Cores:" },
   { ATTRIBUTE_XCDS,          "XCDs:",                   "XCDs" },
   { ATTRIBUTE_LDS_SIZE,      "LDS size:",               "LDS:" },
   { ATTRIBUTE_STREAMINGMP,   "SMs:",                    "SMs:" },

src/cuda/cuda.cpp

@@ -1,6 +1,3 @@
-
-// patched cuda.cpp for cuda13 by cloudy
-
 #include <cuda_runtime.h>
 #include <cstring>
 #include <cstdlib>
@@ -17,20 +14,25 @@ bool print_gpu_cuda(struct gpu_info* gpu) {
   char* cc = get_str_cc(gpu->arch);
   printf("%s (Compute Capability %s)\n", gpu->name, cc);
   free(cc);
+
   return true;
 }
 
 struct cache* get_cache_info(cudaDeviceProp prop) {
   struct cache* cach = (struct cache*) emalloc(sizeof(struct cache));
+
   cach->L2 = (struct cach*) emalloc(sizeof(struct cach));
   cach->L2->size = prop.l2CacheSize;
   cach->L2->num_caches = 1;
   cach->L2->exists = true;
+
   return cach;
 }
 
 int get_tensor_cores(struct uarch* arch, int sm, int major) {
   if(major == 7) {
+    // TU116 does not have tensor cores!
+    // https://www.anandtech.com/show/13973/nvidia-gtx-1660-ti-review-feat-evga-xc-gaming/2
     if (is_chip_TU116(arch))
       return 0;
     return sm * 8;
@@ -41,57 +43,57 @@ int get_tensor_cores(struct uarch* arch, int sm, int major) {
 
 struct topology_c* get_topology_info(struct uarch* arch, cudaDeviceProp prop) {
   struct topology_c* topo = (struct topology_c*) emalloc(sizeof(struct topology_c));
+
   topo->streaming_mp = prop.multiProcessorCount;
   topo->cores_per_mp = _ConvertSMVer2Cores(prop.major, prop.minor);
   topo->cuda_cores = topo->streaming_mp * topo->cores_per_mp;
   topo->tensor_cores = get_tensor_cores(arch, topo->streaming_mp, prop.major);
+
   return topo;
 }
 
 int32_t guess_clock_multipilier(struct gpu_info* gpu, struct memory* mem) {
+  // Guess clock multiplier
   int32_t clk_mul = 1;
+
   int32_t clk8 = abs((mem->freq/8) - gpu->freq);
   int32_t clk4 = abs((mem->freq/4) - gpu->freq);
   int32_t clk2 = abs((mem->freq/2) - gpu->freq);
   int32_t clk1 = abs((mem->freq/1) - gpu->freq);
+
   int32_t min = mem->freq;
   if(clkm_possible_for_uarch(8, gpu->arch) && min > clk8) { clk_mul = 8; min = clk8; }
   if(clkm_possible_for_uarch(4, gpu->arch) && min > clk4) { clk_mul = 4; min = clk4; }
   if(clkm_possible_for_uarch(2, gpu->arch) && min > clk2) { clk_mul = 2; min = clk2; }
   if(clkm_possible_for_uarch(1, gpu->arch) && min > clk1) { clk_mul = 1; min = clk1; }
+
   return clk_mul;
 }
 
 struct memory* get_memory_info(struct gpu_info* gpu, cudaDeviceProp prop) {
   struct memory* mem = (struct memory*) emalloc(sizeof(struct memory));
-  int val = 0;
 
   mem->size_bytes = (unsigned long long) prop.totalGlobalMem;
-
+  mem->freq = prop.memoryClockRate * 0.001f;
-  if (cudaDeviceGetAttribute(&val, cudaDevAttrMemoryClockRate, gpu->idx) == cudaSuccess) {
-      if (val > 1000000)
-          mem->freq = (float)val / 1000000.0f;
-      else
-          mem->freq = (float)val * 0.001f;
-  } else {
-      mem->freq = 0.0f;
-  }
-
   mem->bus_width = prop.memoryBusWidth;
   mem->clk_mul = guess_clock_multipilier(gpu, mem);
   mem->type = guess_memtype_from_cmul_and_uarch(mem->clk_mul, gpu->arch);
 
-  if (mem->clk_mul > 0)
+  // Fix frequency returned from CUDA to show real frequency
-      mem->freq = mem->freq / mem->clk_mul;
+  mem->freq = mem->freq  / mem->clk_mul;
 
   return mem;
 }
 
+// Compute peak performance when using CUDA cores
 int64_t get_peak_performance_cuda(struct gpu_info* gpu) {
   return gpu->freq * 1000000 * gpu->topo_c->cuda_cores * 2;
 }
 
+// Compute peak performance when using tensor cores
 int64_t get_peak_performance_tcu(cudaDeviceProp prop, struct gpu_info* gpu) {
+  // Volta / Turing tensor cores performs 4x4x4 FP16 matrix multiplication
+  // Ampere tensor cores performs 8x4x8 FP16 matrix multiplicacion
   if(prop.major == 7) return gpu->freq * 1000000 * 4 * 4 * 4  * 2 * gpu->topo_c->tensor_cores;
   else if(prop.major == 8) return gpu->freq * 1000000 * 8 * 4 * 8 * 2 * gpu->topo_c->tensor_cores;
   else return 0;
@@ -113,7 +115,8 @@ struct gpu_info* get_gpu_info_cuda(struct pci_dev *devices, int gpu_idx) {
   }
 
   int num_gpus = -1;
-  cudaError_t err = cudaGetDeviceCount(&num_gpus);
+  cudaError_t err = cudaSuccess;
+  err = cudaGetDeviceCount(&num_gpus);
 
   if(gpu_idx == 0) {
     printf("\r%*c\r", (int) strlen(CUDA_DRIVER_START_WARNING), ' ');
@@ -131,6 +134,7 @@ struct gpu_info* get_gpu_info_cuda(struct pci_dev *devices, int gpu_idx) {
   }
 
   if(gpu->idx+1 > num_gpus) {
+    // Master is trying to query an invalid GPU
     return NULL;
   }
 
@@ -140,25 +144,15 @@ struct gpu_info* get_gpu_info_cuda(struct pci_dev *devices, int gpu_idx) {
     return NULL;
   }
 
-  int core_clk = 0;
+  gpu->freq = deviceProp.clockRate * 1e-3f;
-  if (cudaDeviceGetAttribute(&core_clk, cudaDevAttrClockRate, gpu->idx) == cudaSuccess) {
-      if (core_clk > 1000000)
-          gpu->freq = core_clk / 1000000.0f;
-      else
-          gpu->freq = core_clk * 0.001f;
-  } else {
-      gpu->freq = 0.0f;
-  }
-
   gpu->vendor = GPU_VENDOR_NVIDIA;
-  gpu->name = (char *) emalloc(strlen(deviceProp.name) + 1);
+  gpu->name = (char *) emalloc(sizeof(char) * (strlen(deviceProp.name) + 1));
   strcpy(gpu->name, deviceProp.name);
 
   if((gpu->pci = get_pci_from_pciutils(devices, PCI_VENDOR_ID_NVIDIA, gpu_idx)) == NULL) {
     printErr("Unable to find a valid device for vendor id 0x%.4X using pciutils", PCI_VENDOR_ID_NVIDIA);
     return NULL;
   }
-
   gpu->arch = get_uarch_from_cuda(gpu);
   gpu->cach = get_cache_info(deviceProp);
   gpu->mem = get_memory_info(gpu, deviceProp);
@@ -169,7 +163,19 @@ struct gpu_info* get_gpu_info_cuda(struct pci_dev *devices, int gpu_idx) {
   return gpu;
 }
 
-char* get_str_sm(struct gpu_info* gpu) { return get_str_generic(gpu->topo_c->streaming_mp); }
+char* get_str_sm(struct gpu_info* gpu) {
-char* get_str_cores_sm(struct gpu_info* gpu) { return get_str_generic(gpu->topo_c->cores_per_mp); }
+  return get_str_generic(gpu->topo_c->streaming_mp);
-char* get_str_cuda_cores(struct gpu_info* gpu) { return get_str_generic(gpu->topo_c->cuda_cores); }
+}
-char* get_str_tensor_cores(struct gpu_info* gpu) { return get_str_generic(gpu->topo_c->tensor_cores); }
+
+char* get_str_cores_sm(struct gpu_info* gpu) {
+  return get_str_generic(gpu->topo_c->cores_per_mp);
+}
+
+char* get_str_cuda_cores(struct gpu_info* gpu) {
+  return get_str_generic(gpu->topo_c->cuda_cores);
+}
+
+char* get_str_tensor_cores(struct gpu_info* gpu) {
+  return get_str_generic(gpu->topo_c->tensor_cores);
+}
+