Fixing topology detection memory access and CU masking for multi XCD GPUs (#116)

* Fixing potential out-of-bounds write during topology detection
* Fixing CU_MASK for multi-XCD GPUs
* Adding sub-iterations via NUM_SUBITERATIONS
* Adding support for variable subexecutor Transfers
* Adding healthcheck preset

CHANGELOG.md

@@ -3,6 +3,29 @@
 Documentation for TransferBench is available at
 [https://rocm.docs.amd.com/projects/TransferBench](https://rocm.docs.amd.com/projects/TransferBench).
 
+## v1.51
+
+## Modified
+- CSV output has been modified slightly to match normal terminal output
+- Output for non single stream mode has been changed to match single stream mode (results per Executor)
+
+### Added
+- Support for sub-iterations via NUM_SUBITERATIONS.  This allows for additional looping during an iteration
+  If set to 0, this should infinitely loop (which may be useful for some debug purposes)
+- Support for variable number of subexecutors (currently for GPU-GFX executor only).  Setting subExecutors to
+  0 will run over a range of CUs to use, and report only the results of the best one found. This can be tuned
+  for performance by setting the MIN_VAR_SUBEXEC and MAX_VAR_SUBEXEC environment variables to narrow the
+  search space.  The number of CUs used will be identical for all variable subExecutor transfers
+- Experimental new "healthcheck" preset config which currently only supports MI300 series.  This preset runs
+  through CPU to GPU bandwidth tests and all-to-all XGMI bandwidth tests and compares against expected values
+  Pass criteria limits can be modified (due to platform differences) via the environment variables
+  LIMIT_UDIR (undirectional), LIMIT_BDIR (bidirectional), and LIMIT_A2A (Per GPU-GPU link bandwidth)
+
+### Fixed
+- Fixed out-of-bounds memory access during topology detection that can happen if the number of
+  CPUs is less than the number of NUMA domains
+- Fixed CU masking functionality on multi-XCD architectures (e.g. MI300)
+
 ## v1.50
 
 ### Added

README.md

@@ -68,7 +68,8 @@ make
   information
 * You can use several preset configurations instead of a configuration file:
   * `a2a` : All-to-all benchmark test
-  * `cmdline`: Take in Transfers to run from command-line instead of via file
+  * `cmdline` : Take in Transfers to run from command-line instead of via file
+  * `healthcheck` : Simple health check (supported on MI300 series only)
   * `p2p`    : Peer-to-peer benchmark test
   * `pcopy`  : Benchmark parallel copies from a single GPU to other GPUs
   * `rsweep` : Random sweep across possible sets of transfers

src/include/EnvVars.hpp

@@ -29,7 +29,7 @@ THE SOFTWARE.
 #include "Compatibility.hpp"
 #include "Kernels.hpp"
 
-#define TB_VERSION "1.50"
+#define TB_VERSION "1.51"
 
 extern char const MemTypeStr[];
 extern char const ExeTypeStr[];
@@ -84,9 +84,12 @@ public:
   int gfxUnroll;         // GFX-kernel unroll factor
   int gfxWaveOrder;      // GFX-kernel wavefront ordering
   int hideEnv;           // Skip printing environment variable
+  int minNumVarSubExec;  // Minimum # of subexecutors to use for variable subExec Transfers
+  int maxNumVarSubExec;  // Maximum # of subexecutors to use for variable subExec Transfers (0 to use device limit)
   int numCpuDevices;     // Number of CPU devices to use (defaults to # NUMA nodes detected)
   int numGpuDevices;     // Number of GPU devices to use (defaults to # HIP devices detected)
   int numIterations;     // Number of timed iterations to perform.  If negative, run for -numIterations seconds instead
+  int numSubIterations;  // Number of subiterations to perform
   int numWarmups;        // Number of un-timed warmup iterations to perform
   int outputToCsv;       // Output in CSV format
   int samplingFactor;    // Affects how many different values of N are generated (when N set to 0)
@@ -188,9 +191,12 @@ public:
     gfxUnroll         = GetEnvVar("GFX_UNROLL"          , defaultGfxUnroll);
     gfxWaveOrder      = GetEnvVar("GFX_WAVE_ORDER"      , 0);
     hideEnv           = GetEnvVar("HIDE_ENV"            , 0);
+    minNumVarSubExec  = GetEnvVar("MIN_VAR_SUBEXEC"     , 1);
+    maxNumVarSubExec  = GetEnvVar("MAX_VAR_SUBEXEC"     , 0);
     numCpuDevices     = GetEnvVar("NUM_CPU_DEVICES"     , numDetectedCpus);
     numGpuDevices     = GetEnvVar("NUM_GPU_DEVICES"     , numDetectedGpus);
     numIterations     = GetEnvVar("NUM_ITERATIONS"      , DEFAULT_NUM_ITERATIONS);
+    numSubIterations  = GetEnvVar("NUM_SUBITERATIONS"   , 1);
     numWarmups        = GetEnvVar("NUM_WARMUPS"         , DEFAULT_NUM_WARMUPS);
     outputToCsv       = GetEnvVar("OUTPUT_TO_CSV"       , 0);
     samplingFactor    = GetEnvVar("SAMPLING_FACTOR"     , DEFAULT_SAMPLING_FACTOR);
@@ -299,6 +305,24 @@ public:
     }
     else fillPattern.clear();
 
+    // Figure out number of xccs per device
+    int maxNumXccs = 64;
+    xccIdsPerDevice.resize(numGpuDevices);
+    for (int i = 0; i < numGpuDevices; i++)
+    {
+      int* data;
+      HIP_CALL(hipSetDevice(i));
+      HIP_CALL(hipHostMalloc((void**)&data, maxNumXccs * sizeof(int)));
+      CollectXccIdsKernel<<<maxNumXccs, 1>>>(data);
+      HIP_CALL(hipDeviceSynchronize());
+
+      xccIdsPerDevice[i].clear();
+      for (int j = 0; j < maxNumXccs; j++)
+        xccIdsPerDevice[i].insert(data[j]);
+
+      HIP_CALL(hipHostFree(data));
+    }
+
     // Check for CU mask
     cuMask.clear();
     char* cuMaskStr = getenv("CU_MASK");
@@ -308,6 +332,7 @@ public:
       printf("[WARN] CU_MASK is not supported in CUDA\n");
 #else
       std::vector<std::pair<int, int>> ranges;
+      int numXccs = (xccIdsPerDevice.size() > 0 ? xccIdsPerDevice[0].size() : 1);
       int maxCU = 0;
       char* token = strtok(cuMaskStr, ",");
       while (token)
@@ -330,34 +355,20 @@ public:
         }
         token = strtok(NULL, ",");
       }
-      cuMask.resize(maxCU / 32 + 1, 0);
+      cuMask.resize(2 * numXccs, 0);
 
       for (auto range : ranges)
       {
         for (int i = range.first; i <= range.second; i++)
         {
-          cuMask[i / 32] |= (1 << (i % 32));
+          for (int x = 0; x < numXccs; x++)
+          {
+            int targetBit = i * numXccs + x;
+            cuMask[targetBit/32] |= (1<<(targetBit%32));
           }
         }
-#endif
       }
-
-    // Figure out number of xccs per device
-    int maxNumXccs = 64;
-    xccIdsPerDevice.resize(numGpuDevices);
-    for (int i = 0; i < numGpuDevices; i++)
-    {
-      int* data;
-      HIP_CALL(hipSetDevice(i));
-      HIP_CALL(hipHostMalloc((void**)&data, maxNumXccs * sizeof(int)));
-      CollectXccIdsKernel<<<maxNumXccs, 1>>>(data);
-      HIP_CALL(hipDeviceSynchronize());
-
-      xccIdsPerDevice[i].clear();
-      for (int j = 0; j < maxNumXccs; j++)
-        xccIdsPerDevice[i].insert(data[j]);
-
-      HIP_CALL(hipHostFree(data));
+#endif
     }
 
     // Parse preferred XCC table (if provided
@@ -429,6 +440,11 @@ public:
       printf("[ERROR] BLOCK_ORDER must be 0 (Sequential), 1 (Interleaved), or 2 (Random)\n");
       exit(1);
     }
+    if (minNumVarSubExec  < 1)
+    {
+      printf("[ERROR] Minimum number of subexecutors for variable subexector transfers must be at least 1\n");
+      exit(1);
+    }
     if (numWarmups < 0)
     {
       printf("[ERROR] NUM_WARMUPS must be set to a non-negative number\n");
@@ -524,8 +540,10 @@ public:
     // Determine how many CPUs exit per NUMA node (to avoid executing on NUMA without CPUs)
     numCpusPerNuma.resize(numDetectedCpus);
     int const totalCpus = numa_num_configured_cpus();
-    for (int i = 0; i < totalCpus; i++)
-      numCpusPerNuma[numa_node_of_cpu(i)]++;
+    for (int i = 0; i < totalCpus; i++) {
+      int node = numa_node_of_cpu(i);
+      if (node >= 0) numCpusPerNuma[node]++;
+    }
 
     // Build array of wall clock rates per GPU device
     wallClockPerDeviceMhz.resize(numDetectedGpus);
@@ -583,9 +601,12 @@ public:
     printf(" GFX_SINGLE_TEAM        - Have subexecutors work together on full array instead of working on individual disjoint subarrays\n");
     printf(" GFX_WAVE_ORDER         - Stride pattern for GFX kernel (0=UWC,1=UCW,2=WUC,3=WCU,4=CUW,5=CWU)\n");
     printf(" HIDE_ENV               - Hide environment variable value listing\n");
+    printf(" MIN_VAR_SUBEXEC        - Minumum # of subexecutors to use for variable subExec Transfers\n");
+    printf(" MAX_VAR_SUBEXEC        - Maximum # of subexecutors to use for variable subExec Transfers (0 for device limits)\n");
     printf(" NUM_CPU_DEVICES=X      - Restrict number of CPUs to X.  May not be greater than # detected NUMA nodes\n");
     printf(" NUM_GPU_DEVICES=X      - Restrict number of GPUs to X.  May not be greater than # detected HIP devices\n");
     printf(" NUM_ITERATIONS=I       - Perform I timed iteration(s) per test\n");
+    printf(" NUM_SUBITERATIONS=S    - Perform S sub-iteration(s) per iteration. Must be non-negative\n");
     printf(" NUM_WARMUPS=W          - Perform W untimed warmup iteration(s) per test\n");
     printf(" OUTPUT_TO_CSV          - Outputs to CSV format if set\n");
     printf(" SAMPLING_FACTOR=F      - Add F samples (when possible) between powers of 2 when auto-generating data sizes\n");
@@ -649,6 +670,12 @@ public:
                                                                        gfxWaveOrder == 3 ? "Wavefront,CU,Unroll" :
                                                                        gfxWaveOrder == 4 ? "CU,Unroll,Wavefront" :
                                                                                            "CU,Wavefront,Unroll")));
+    PRINT_EV("MIN_VAR_SUBEXEC", minNumVarSubExec,
+             std::string("Using at least ") + std::to_string(minNumVarSubExec) + " subexecutor(s) for variable subExec tranfers");
+    PRINT_EV("MAX_VAR_SUBEXEC", maxNumVarSubExec,
+             maxNumVarSubExec ?
+             std::string("Using at most ") + std::to_string(maxNumVarSubExec) + " subexecutor(s) for variable subExec tranfers" :
+             "Using up to maximum device subexecutors for variable subExec tranfers");
     PRINT_EV("NUM_CPU_DEVICES", numCpuDevices,
              std::string("Using ") + std::to_string(numCpuDevices) + " CPU devices");
     PRINT_EV("NUM_GPU_DEVICES", numGpuDevices,
@@ -656,6 +683,8 @@ public:
     PRINT_EV("NUM_ITERATIONS", numIterations,
              std::string("Running ") + std::to_string(numIterations > 0 ? numIterations : -numIterations) + " "
              + (numIterations > 0 ? " timed iteration(s)" : "seconds(s) per Test"));
+    PRINT_EV("NUM_SUBITERATIONS", numSubIterations,
+             std::string("Running ") + (numSubIterations == 0 ? "infinite" : std::to_string(numSubIterations)) + " subiterations");
     PRINT_EV("NUM_WARMUPS", numWarmups,
              std::string("Running " + std::to_string(numWarmups) + " warmup iteration(s) per Test"));
     PRINT_EV("SHARED_MEM_BYTES", sharedMemBytes,
@@ -828,36 +857,27 @@ public:
   std::string GetCuMaskDesc() const
   {
     std::vector<std::pair<int, int>> runs;
-
+    int numXccs = (xccIdsPerDevice.size() > 0 ? xccIdsPerDevice[0].size() : 1);
     bool inRun = false;
     std::pair<int, int> curr;
     int used = 0;
-    for (int i = 0; i < cuMask.size(); i++)
-    {
-      for (int j = 0; j < 32; j++)
-      {
-        if (cuMask[i] & (1 << j))
-        {
+    for (int targetBit = 0; targetBit < cuMask.size() * 32; targetBit += numXccs) {
+      if (cuMask[targetBit/32] & (1 << (targetBit%32))) {
         used++;
-          if (!inRun)
-          {
+        if (!inRun) {
           inRun = true;
-            curr.first = i * 32 + j;
+          curr.first = targetBit / numXccs;
         }
-        }
-        else
-        {
-          if (inRun)
-          {
+      } else {
+        if (inRun) {
           inRun = false;
-            curr.second = i * 32 + j - 1;
+          curr.second = targetBit / numXccs - 1;
           runs.push_back(curr);
         }
       }
     }
-    }
     if (inRun)
-      curr.second = cuMask.size() * 32 - 1;
+      curr.second = (cuMask.size() * 32) / numXccs - 1;
 
     std::string result = "CUs used: (" + std::to_string(used) + ") ";
     for (int i = 0; i < runs.size(); i++)

src/include/Kernels.hpp

@@ -174,7 +174,7 @@ template <>           __device__ __forceinline__ float4 MemsetVal(){ return make
 
 template <int BLOCKSIZE, int UNROLL>
 __global__ void __launch_bounds__(BLOCKSIZE)
-  GpuReduceKernel(SubExecParam* params, int waveOrder)
+  GpuReduceKernel(SubExecParam* params, int waveOrder, int numSubIterations)
 {
   int64_t startCycle;
   if (threadIdx.x == 0) startCycle = GetTimestamp();
@@ -216,13 +216,14 @@ __global__ void __launch_bounds__(BLOCKSIZE)
   case 5: /* C,W,U */ teamStride = 1; waveStride = nTeams; unrlStride = nTeams * nWaves;  teamStride2 = 1;      waveStride2 = nTeams; break;
   }
 
+  int subIterations = 0;
+  while (1) {
     // First loop: Each wavefront in the team works on UNROLL float4s per thread
     size_t const loop1Stride = nTeams * nWaves * UNROLL * warpSize;
     size_t const loop1Limit  = numFloat4 / loop1Stride * loop1Stride;
     {
       float4 val[UNROLL];
-    if (numSrcs == 0)
-    {
+      if (numSrcs == 0) {
         #pragma unroll
         for (int u = 0; u < UNROLL; u++)
           val[u] = MemsetVal<float4>();
@@ -296,6 +297,9 @@ __global__ void __launch_bounds__(BLOCKSIZE)
       }
     }
 
+    if (++subIterations == numSubIterations) break;
+  }
+
   // Wait for all threads to finish
   __syncthreads();
   if (threadIdx.x == 0)
@@ -308,7 +312,7 @@ __global__ void __launch_bounds__(BLOCKSIZE)
   }
 }
 
-typedef void (*GpuKernelFuncPtr)(SubExecParam*, int);
+typedef void (*GpuKernelFuncPtr)(SubExecParam*, int, int);
 
 #define GPU_KERNEL_UNROLL_DECL(BLOCKSIZE) \
   {GpuReduceKernel<BLOCKSIZE, 1>,  \

src/include/TransferBench.hpp

@@ -158,6 +158,25 @@ struct ExecutorInfo
   double totalTime;
 };
 
+struct ExeResult
+{
+  double bandwidthGbs;
+  double durationMsec;
+  double sumBandwidthGbs;
+  size_t totalBytes;
+  std::vector<int> transferIdx;
+};
+
+struct TestResults
+{
+  size_t numTimedIterations;
+  size_t totalBytesTransferred;
+  double totalBandwidthCpu;
+  double totalDurationMsec;
+  double overheadMsec;
+  std::map<std::pair<ExeType, int>, ExeResult> exeResults;
+};
+
 typedef std::pair<ExeType, int> Executor;
 typedef std::map<Executor, ExecutorInfo> TransferMap;
 
@@ -179,7 +198,8 @@ void ParseTransfers(EnvVars const& ev, char* line, std::vector<Transfer>& transf
 void ExecuteTransfers(EnvVars const& ev, int const testNum, size_t const N,
                       std::vector<Transfer>& transfers, bool verbose = true,
                       double* totalBandwidthCpu = nullptr);
-
+TestResults ExecuteTransfersImpl(EnvVars const& ev, std::vector<Transfer>& transfers);
+void ReportResults(EnvVars const& ev, std::vector<Transfer> const& transfers, TestResults const results);
 void EnablePeerAccess(int const deviceId, int const peerDeviceId);
 void AllocateMemory(MemType memType, int devIndex, size_t numBytes, void** memPtr);
 void DeallocateMemory(MemType memType, void* memPtr, size_t const size = 0);
@@ -192,6 +212,7 @@ void RunAllToAllBenchmark(EnvVars const& ev, size_t const numBytesPerTransfer, i
 void RunSchmooBenchmark(EnvVars const& ev, size_t const numBytesPerTransfer, int const localIdx, int const remoteIdx, int const maxSubExecs);
 void RunRemoteWriteBenchmark(EnvVars const& ev, size_t const numBytesPerTransfer, int numSubExecs, int const srcIdx, int minGpus, int maxGpus);
 void RunParallelCopyBenchmark(EnvVars const& ev, size_t const numBytesPerTransfer, int numSubExecs, int const srcIdx, int minGpus, int maxGpus);
+void RunHealthCheck(EnvVars ev);
 
 std::string GetLinkTypeDesc(uint32_t linkType, uint32_t hopCount);