Project 2: Shenyue Chen

README.md

@@ -3,12 +3,122 @@ CUDA Stream Compaction
 
 **University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2**
 
-* (TODO) YOUR NAME HERE
-  * (TODO) [LinkedIn](), [personal website](), [twitter](), etc.
-* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab)
+* Shenyue Chen
+  * [LinkedIn](https://www.linkedin.com/in/shenyue-chen-5b2728119/), [personal website](http://github.com/EvsChen)
+* Tested on: Windows 10, Intel Xeon Platinum 8259CL @ 2.50GHz 16GB, Tesla T4 (AWS g4dn-xlarge)
+
+### Features
+* Implementation of cpu scan, cpu compact, naive scan, work-efficient scan and work-efficient compact
+* Optimization of the work-efficient scan algorithm by launching only necessary number of threads in `upSweep` and `downSweep`
+    * Map thread index to the actual index by `interval * idx + interval - 1`, where interval is `1 << iteration`
+    * For example, for N = 8, 4 threads is launched in the first iteration, 2 launched in the secondm etc.
+
+### Performance analysis
+Block size: 128
+
+Lengh of array: from (2^10 - 2^21)
+
+**All the time measured in this section is the average of 100 tests, to avoid caching of functions**
+
+As N increases, the time for cpu algorithms increases in a exponential manner while the GPU algorithms increases much slower.
+
+<p align="center">
+<image src="doc/scan_time.png">
+</p>
+
+<p align="center">
+<image src="doc/compact.png">
+</p>
+
+For the GPU algorithms only, the naive algorithm performs the best when N is small. But thrust scan turns out to be the best when N becomes larger.
+<p align="center">
+<image src="doc/gpu_scan.png">
+</p>
+
+In my experiments, there are no obvious difference for the NPOT version of the work efficient scan algorithm.
+<p align="center">
+<image src="doc/scan_time_npot.png">
+</p>
+
+Similar things happen for the thrust scan.
+<p align="center">
+<image src="doc/thrust_scan_time_npot.png">
+</p>
+
+
+
+
+### Sample output
+I tested each of the algorithm for 100 times and include some additional information.
+```
+****************
+** SCAN TESTS **
+****************
+    [  41  23  25   1   5  46  28  37  30  42  42  25  35 ...  38   0 ]
+==== cpu scan, power-of-two ====
+    Time record is [11.006, 11.431, 11.774, 13.613, 14.736, 11.656, 11.874, 11.659, 11.823, 12.829, 11.737, 11.989, 11.872, ... 25.619]
+   elapsed time: 13.311ms    (std::chrono Measured)
+    [   0  41  64  89  90  95 141 169 206 236 278 320 345 ... 51331714 51331752 ]
+==== cpu scan, non-power-of-two ====
+    Time record is [6.4447, 6.2375, 20.608, 8.2194, 4.5156, 5.2029, 4.3476, 3.7569, 3.7672, 3.9463, 3.8041, 6.7647, 8.9613, ... 3.7673]
+   elapsed time: 4.7256ms    (std::chrono Measured)
+    [   0  41  64  89  90  95 141 169 206 236 278 320 345 ... 51331652 51331692 ]
+    passed
+==== naive scan, power-of-two ====
+    Time record is [1.6997, 1.6947, 1.6957, 1.6964, 1.6937, 1.6972, 1.6957, 1.6955, 1.6977, 1.6956, 1.6957, 1.697, 1.6957, ... 1.5053]
+   elapsed time: 1.5804ms    (CUDA Measured)
+    passed
+==== naive scan, non-power-of-two ====
+    Time record is [1.505, 1.5154, 1.5114, 1.5073, 1.5134, 1.5131, 1.5095, 1.5173, 1.5183, 1.5173, 1.5181, 1.5193, 1.5177, ... 1.5286]
+   elapsed time: 1.5617ms    (CUDA Measured)
+    passed
+==== work-efficient scan, power-of-two ====
+    Time record is [1.0465, 0.9728, 0.92182, 0.9257, 0.93184, 2.3247, 0.92374, 0.9345, 0.92896, 0.92176, 0.92269, 0.92266, 0.93555, ... 0.9264]
+   elapsed time: 0.97037ms    (CUDA Measured)
+    passed
+==== work-efficient scan, non-power-of-two ====
+    Time record is [0.93405, 0.92058, 0.92365, 0.91706, 0.93229, 0.92541, 0.9175, 0.93056, 0.9216, 0.9176, 0.91802, 0.93424, 0.91955, ... 0.91955]
+   elapsed time: 0.92851ms    (CUDA Measured)
+    passed
+==== thrust scan, power-of-two ====
+    Time record is [0.27674, 0.29424, 0.37693, 0.28387, 0.26618, 0.26726, 0.29562, 0.27222, 0.27443, 0.29901, 0.31325, 0.30925, 0.27082, ... 0.26301]
+   elapsed time: 0.30972ms    (CUDA Measured)
+    passed
+==== thrust scan, non-power-of-two ====
+    Time record is [0.2639, 0.36099, 0.28035, 0.26765, 0.39936, 0.30883, 0.29104, 0.27306, 0.26934, 0.2631, 0.29914, 0.26224, 0.2863, ... 0.29773]
+   elapsed time: 0.31379ms    (CUDA Measured)
+    passed
+
+*****************************
+** STREAM COMPACTION TESTS **
+*****************************
+    [   0   1   1   3   0   1   0   2   3   3   1   1   1 ...   3   0 ]
+==== cpu compact without scan, power-of-two ====
+    Time record is [7.5451, 5.9954, 5.9785, 5.7061, 6.2471, 6.2241, 5.9236, 5.7947, 6.4508, 5.8406, 5.8629, 5.7438, 5.7671, ... 5.7358]
+   elapsed time: 6.2666ms    (std::chrono Measured)
+    [   1   1   3   1   2   3   3   1   1   1   3   1   2 ...   1   3 ]
+    passed
+==== cpu compact without scan, non-power-of-two ====
+    Time record is [7.3508, 8.2913, 5.9938, 5.9339, 5.8238, 5.7023, 5.9023, 5.8208, 6.926, 6.5035, 5.7348, 6.8928, 7.7841, ... 5.8204]
+   elapsed time: 6.2747ms    (std::chrono Measured)
+    [   1   1   3   1   2   3   3   1   1   1   3   1   2 ...   2   3 ]
+    passed
+==== cpu compact with scan ====
+    Time record is [28.751, 24.819, 23.735, 23.725, 23.829, 23.911, 23.968, 26.001, 24.981, 24.231, 23.606, 24.335, 23.51, ... 26.916]
+   elapsed time: 24.973ms    (std::chrono Measured)
+    [   1   1   3   1   2   3   3   1   1   1   3   1   2 ...   1   3 ]
+    passed
+==== work-efficient compact, power-of-two ====
+    Time record is [1.9505, 1.6153, 1.6267, 1.6086, 1.6013, 1.6535, 1.6727, 1.7992, 1.7735, 2.1627, 1.7832, 1.7375, 1.7575, ... 1.682]
+   elapsed time: 1.8556ms    (CUDA Measured)
+    passed
+==== work-efficient compact, non-power-of-two ====
+    Time record is [1.6855, 1.9108, 1.7044, 1.6872, 1.7303, 1.9761, 1.7651, 1.9041, 1.6835, 1.6977, 1.7791, 1.6812, 1.7178, ... 1.6495]
+   elapsed time: 1.7805ms    (CUDA Measured)
+    passed
+Result for n = 2097152 is :
+    Time record is [13.311, 4.7256, 1.5804, 1.5617, 0.97037, 0.92851, 0.30972, 0.31379, 6.2666, 6.2747, 24.973, 1.8556, 1.7805, ... 0]
+```
 
-### (TODO: Your README)
 
-Include analysis, etc. (Remember, this is public, so don't put
-anything here that you don't want to share with the world.)
 

doc/analysis.py

@@ -0,0 +1,75 @@
+import csv
+import matplotlib.pyplot as plt
+
+data = []
+with open('lab2.csv', 'r') as csvfile:
+  spamreader = csv.reader(csvfile, delimiter=',')
+  for row in spamreader:
+    drow = []
+    for data_str in row:
+      drow.append(float(data_str))
+    data.append(drow)
+
+start = 10
+end = 21
+
+cpu_scan = []
+naive_scan = []
+efficient_scan = []
+efficient_scan_npot = []
+thrust_scan = []
+thrust_scan_npot = []
+cpu_compact = []
+efficient_compact = []
+
+x = []
+
+# import pdb; pdb.set_trace()
+
+for i in range(start, end + 1):
+  x.append(i)
+  row = data[i - 10]
+  cpu_scan.append(row[0])
+  naive_scan.append(row[2])
+  efficient_scan.append(row[4])
+  efficient_scan_npot.append(row[6])
+  thrust_scan.append(row[6])
+  thrust_scan_npot.append(row[7])
+  cpu_compact.append(row[8])
+  efficient_compact.append(row[11])  
+
+fig, ax = plt.subplots()
+
+### Scan 
+# plt.plot(x, cpu_scan, label="cpu_scan")
+# plt.plot(x, naive_scan, label="naive_scan")
+# plt.plot(x, efficient_scan, label="work_efficient")
+# plt.plot(x, thrust_scan, label="thrust_scane")
+# plt.title("Scan time")
+
+### NPOT
+# plt.plot(x, efficient_scan, label="work-efficient scan")
+# plt.plot(x, efficient_scan_npot, label="work-efficient scan NPOT")
+# plt.title("Scan time")
+
+
+### Thrust NPOT
+# plt.plot(x, thrust_scan, label="thrust scan")
+# plt.plot(x, thrust_scan_npot, label="thrust scan NPOT")
+# plt.title("Scan time")
+
+### Compact
+# plt.plot(x, cpu_compact, label="cpu compact")
+# plt.plot(x, efficient_compact, label="work efficient compact")
+# plt.title("Compact time")
+
+### GPU
+plt.plot(x, naive_scan, label="naive_scan")
+plt.plot(x, efficient_scan, label="work_efficient")
+plt.plot(x, thrust_scan, label="thrust_scan")
+plt.title("GPU Scan time")
+
+plt.xlabel("Number (in base 2)")
+plt.ylabel("Time (ms)")
+plt.legend()
+plt.show()

doc/lab2.csv

@@ -0,0 +1,12 @@
+0.001885, 0.001899, 0.036519, 0.03666, 0.10034, 0.12692, 0.19843, 0.18431, 0.002746, 0.004293, 0.007446, 0.2877, 0.32142
+0.005164, 0.003933, 0.05982, 0.064216, 0.16541, 0.13125, 0.15975, 0.1403, 0.003983, 0.005303, 0.010544, 0.30586, 0.37939
+0.009546, 0.007338, 0.046396, 0.046294, 0.13717, 0.1225, 0.14251, 0.12847, 0.020095, 0.008572, 0.045547, 0.3119, 0.32465
+0.009546, 0.007338, 0.046396, 0.046294, 0.13717, 0.1225, 0.14251, 0.12847, 0.020095, 0.008572, 0.045547, 0.3119, 0.32465
+0.032347, 0.03035, 0.057651, 0.057629, 0.14566, 0.1432, 0.17856, 0.17249, 0.062693, 0.068325, 0.141, 0.39171, 0.40263
+0.072447, 0.066555, 0.065433, 0.065277, 0.16558, 0.17479, 0.16487, 0.20362, 0.12599, 0.11522, 0.27981, 0.34057, 0.50167
+0.13301, 0.1447, 0.087247, 0.087203, 0.16367, 0.19217, 0.13575, 0.18643, 0.28578, 0.25115, 0.55336, 0.44775, 0.34398
+0.28354, 0.33327, 0.12242, 0.11939, 0.19577, 0.18088, 0.1958, 0.15035, 0.70451, 0.6561, 1.6623, 0.51467, 0.52659
+1.4489, 0.57192, 0.20105, 0.18198, 0.21742, 0.25338, 0.64729, 0.28838, 0.79568, 0.85801, 3.0992, 0.55925, 0.56684
+3.1769, 1.1265, 0.35451, 0.3327, 0.31406, 0.34869, 0.29619, 0.31233, 1.602, 1.6526, 6.3687, 0.68551, 0.66141
+6.3527, 2.0997, 0.9041, 0.8269, 0.34781, 0.3672, 0.28274, 0.28195, 3.196, 3.239, 12.361, 0.89818, 0.90519
+13.311, 4.7256, 1.5804, 1.5617, 0.97037, 0.92851, 0.30972, 0.31379, 6.2666, 6.2747, 24.973, 1.8556, 1.7805