- Timestamp:
- Aug 31, 2023, 11:31:15 PM (2 years ago)
- Branches:
- master
- Children:
- 950c58e
- Parents:
- 92355883 (diff), 686912c (diff)
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doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/plotData.py
r92355883 r2a301ff 60 60 name = "Aggregate Lock" 61 61 var_name = "" 62 experiment_duration = 10.0 62 63 sendData = [0.0 for j in range(numVariants)] 63 64 data = [[0.0 for i in range(len(procs))] for j in range(numVariants)] … … 80 81 81 82 lineArr = line.split() 82 tempData[count] = float(lineArr[-1]) 83 tempData[count] = float(lineArr[-1]) / experiment_duration 83 84 count += 1 84 85 if count == numTimes: … … 86 87 data[currVariant][procCount] = currMedian 87 88 lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData)) 88 bars[currVariant][0][procCount] = currMedian - lower89 bars[currVariant][1][procCount] = upper - currMedian89 bars[currVariant][0][procCount] = max( 0, currMedian - lower ) 90 bars[currVariant][1][procCount] = max( 0, upper - currMedian ) 90 91 count = 0 91 92 procCount += 1 … … 99 100 fig, ax = plt.subplots(layout='constrained') 100 101 plt.title(name + " Benchmark: " + str(currLocks) + " Locks") 101 plt.ylabel("Throughput ( entries)")102 plt.ylabel("Throughput (critical section entries per second)") 102 103 plt.xlabel("Cores") 103 104 for idx, arr in enumerate(data):
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