tensorflow / networking
Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

Intro
  • Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
  • Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
  • Conditional complexity is measured at the unit level (methods, functions...).
  • Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).
Learn more...
Conditional Complexity Overall
  • There are 409 units with 5,662 lines of code in units (59.1% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 12 medium complex units (1,125 lines of code)
    • 28 simple units (1,230 lines of code)
    • 369 very simple units (3,307 lines of code)
0% | 0% | 19% | 21% | 58%
Legend:
51+
26-50
11-25
6-10
1-5
Alternative Visuals
Conditional Complexity per Extension
51+
26-50
11-25
6-10
1-5
cc0% | 0% | 21% | 23% | 55%
py0% | 0% | 16% | 22% | 61%
h0% | 0% | 0% | 0% | 100%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
tensorflow_networking/mpi_collectives0% | 0% | 44% | 9% | 46%
tensorflow_networking/mpi_collectives/kernels0% | 0% | 49% | 8% | 42%
tensorflow_networking/gdr0% | 0% | 37% | 27% | 34%
tensorflow_networking/verbs0% | 0% | 5% | 24% | 69%
tensorflow_networking/seastar0% | 0% | 4% | 24% | 71%
ROOT0% | 0% | 22% | 25% | 51%
tensorflow_networking/mpi0% | 0% | 0% | 36% | 63%
tensorflow_networking/mpi_collectives/python0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
MPIResponse ConstructMPIResponse()
in tensorflow_networking/mpi_collectives/mpi_ops.cc
104 19 2
void PerformCollectiveOp()
in tensorflow_networking/mpi_collectives/mpi_ops.cc
80 19 2
void BackgroundThreadLoop()
in tensorflow_networking/mpi_collectives/mpi_ops.cc
140 19 0
MPIResponse ConstructMPIResponse()
in tensorflow_networking/mpi_collectives/kernels/mpi_ops.cc
104 19 2
void PerformCollectiveOp()
in tensorflow_networking/mpi_collectives/kernels/mpi_ops.cc
80 19 2
void BackgroundThreadLoop()
in tensorflow_networking/mpi_collectives/kernels/mpi_ops.cc
140 19 0
void GdrMemoryManager::Run()
in tensorflow_networking/gdr/gdr_memory_manager.cc
83 19 0
void GdrMemoryManager::TensorFromTransportOptions()
in tensorflow_networking/gdr/gdr_memory_manager.cc
103 18 6
void RdmaAdapter::Process_CQ()
in tensorflow_networking/verbs/rdma.cc
81 14 0
seastar::future SeastarClient::Connection::Read()
in tensorflow_networking/seastar/seastar_client.cc
73 14 0
def get_var()
in configure.py
54 13 7
void GdrWorker::GrpcRecvTensorAsync()
in tensorflow_networking/gdr/gdr_worker.cc
83 12 4
uint8_t set_gid()
in tensorflow_networking/verbs/rdma.cc
46 10 2
void MPIRendezvousMgr::AddRequest()
in tensorflow_networking/mpi/mpi_rendezvous_mgr.cc
88 10 2
void RdmaTensorResponse::RecvHandler()
in tensorflow_networking/verbs/rdma.cc
60 9 5
bool RdmaMgr::ConnectivityCheck()
in tensorflow_networking/verbs/rdma_mgr.cc
41 9 0
37 9 1
string RdmaMessage::CreateMessage()
in tensorflow_networking/verbs/rdma.cc
50 8 1
string TranslateTask()
in tensorflow_networking/seastar/seastar_channel_cache.cc
22 8 1
void SeastarWorker::RecvTensorAsync()
in tensorflow_networking/seastar/seastar_worker_service.cc
60 8 4