From 60a5ba5120e4fd936cdab66d2c3b0cf8527d7fb2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Constantin=20F=C3=BCrst?= Date: Tue, 12 Dec 2023 17:14:42 +0100 Subject: [PATCH] refactor the benchmark plotters and submit newly plotted graphs --- benchmarks/benchmark-plotters/__init__.py | 0 .../__pycache__/common.cpython-39.pyc | Bin 0 -> 579 bytes benchmarks/benchmark-plotters/common.py | 15 +++++ .../benchmark-plotters/plot-cost-mtsubmit.py | 41 +++++++----- .../plot-perf-enginelocation.py | 37 ++++++----- .../plot-perf-peakthroughput.py | 59 +++++++++++------- .../plot-perf-submitmethod.py | 41 +++++++----- .../plot-opt-submitmethod.png | Bin 35278 -> 39576 bytes .../plot-perf-allnodes-throughput.png | Bin 0 -> 107259 bytes .../plot-perf-allnodethroughput.png | Bin 100543 -> 0 bytes .../plot-perf-enginelocation.png | Bin 39518 -> 40614 bytes .../benchmark-results/plot-perf-mtsubmit.png | Bin 30710 -> 38179 bytes .../plot-perf-smart-throughput.png | Bin 0 -> 123101 bytes .../plot-perf-smartthroughput.png | Bin 118921 -> 0 bytes 14 files changed, 126 insertions(+), 67 deletions(-) create mode 100644 benchmarks/benchmark-plotters/__init__.py create mode 100644 benchmarks/benchmark-plotters/__pycache__/common.cpython-39.pyc create mode 100644 benchmarks/benchmark-plotters/common.py create mode 100644 benchmarks/benchmark-results/plot-perf-allnodes-throughput.png delete mode 100644 benchmarks/benchmark-results/plot-perf-allnodethroughput.png create mode 100644 benchmarks/benchmark-results/plot-perf-smart-throughput.png delete mode 100644 benchmarks/benchmark-results/plot-perf-smartthroughput.png diff --git a/benchmarks/benchmark-plotters/__init__.py b/benchmarks/benchmark-plotters/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/benchmarks/benchmark-plotters/__pycache__/common.cpython-39.pyc b/benchmarks/benchmark-plotters/__pycache__/common.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0b955d558e438b8cefb0708db0fca3ca2a424e2d GIT binary patch literal 579 zcmY*W%}(Pm5FR_tlI2H0)dLcoEA;?*ffYj9J@?XEv?xmB*~V%d7u!MYDm^W)z(epn zNE~wA%kC?1Vr;1b#+varUuMSkm*VJXAIP4&HO3s^uTMT2ad1O!M-&7I_5c|OE+X2Q z7|dW2m%e6aF#(@{C$|?AIwsf%w!EXRbJ#{*)cJkT`QB){czhZEJGeT%mQ;K5MB}o3 zD}Q4?-#E1Wo|6jm#%6~BSS)oTY?g$Qg`8){t}?n^RIAqIlv~4SP6)4g+FFxVS|I^6 zsnxA12{-Z{jg@Ybx(cnd>AWZ_thKq^<=B*!D$J5T?cn9A*3MC1T56?qbG6#^$7NBM z`TGS1{>=#mU`bfUGoR$~L_{9s853|M&>!{#ZqI)5Ef4a*lAEOC92m^tE)k4`;?w_B z3=`IW#imtg3Wt+HAKw>si+x-eQ*8Q3igh0m^)7<(hxJ3*2wdmCj8-|;sIYN|#M>`^ kCOct6W)28VCp_FZBp+r3NyDJzo6@4K@j5)!|K}um1BO$GdjJ3c literal 0 HcmV?d00001 diff --git a/benchmarks/benchmark-plotters/common.py b/benchmarks/benchmark-plotters/common.py new file mode 100644 index 0000000..4f44dd8 --- /dev/null +++ b/benchmarks/benchmark-plotters/common.py @@ -0,0 +1,15 @@ +# calculates throughput in gib/s from the meassured +# transfer duration (in nanoseconds) for a given element +# with the size of this given in bytes +def calc_throughput(size_bytes,time_ns): + time_seconds = time_ns * 1e-9 + size_gib = size_bytes / (1024 ** 3) + throughput_gibs = size_gib / time_seconds + return throughput_gibs + + +# reverse array search: return index of value in array +def index_from_element(value,array): + for (idx,val) in enumerate(array): + if val == value: return idx + return 0 \ No newline at end of file diff --git a/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py b/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py index 7de358f..d9c9e50 100644 --- a/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py +++ b/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py @@ -5,32 +5,35 @@ from itertools import chain import seaborn as sns import matplotlib.pyplot as plt +from common import calc_throughput, index_from_element + runid = "Run ID" x_label = "Thread Count" y_label = "Throughput in GiB/s" var_label = "Thread Counts" thread_counts = ["1t", "2t", "4t", "8t", "12t"] thread_counts_nice = ["1 Thread", "2 Threads", "4 Threads", "8 Threads", "12 Threads"] -engine_counts = ["1mib-1e_PREVENT_FROM_DISPLAYING", "1mib-4e_PREVENT_FROM_DISPLAYING", "1gib-1e", "1gib-4e"] -engine_counts_nice = ["1 E/WQ and Tasksize 1 MiB", "4 E/WQ and Tasksize 1 MiB", "1 E/WQ and Tasksize 1 GiB", "4 E/WQ and Tasksize 1 GiB"] -title = "Total Throughput - 120 Copy Operations split on Threads Intra-Node on DDR" - -index = [runid, x_label, var_label] -data = [] +engine_counts = ["1mib-1e", "1mib-4e", "1gib-1e", "1gib-4e"] +engine_counts_nice = ["1 E/WQ and 1 MiB", "4 E/WQ and 1 MiB", "1 E/WQ and 1 GiB", "4 E/WQ and 1 GiB"] -def calc_throughput(size_bytes,time_ns): - time_seconds = time_ns * 1e-9 - size_gib = size_bytes / (1024 ** 3) - throughput_gibs = size_gib / time_seconds - return throughput_gibs +title = \ + """Total Throughput showing cost of MT Submit\n + Copying 120x split on n Threads Intra-Node on DDR\n + """ +description = \ + """Total Throughput showing cost of MT Submit\n + Running 120 Copy Operations split on n Threads\n + Copying Intra-Node on DDR performed for multiple Configurations\n + """ -def index_from_element(value,array): - for (idx,val) in enumerate(array): - if val == value: return idx - return 0 +index = [runid, x_label, var_label] +data = [] +# loads the measurements from a given file and processes them +# so that they are normalized, meaning that the timings returned +# are nanoseconds per element transfered def load_time_mesurements(file_path): with open(file_path, 'r') as file: data = json.load(file) @@ -48,6 +51,11 @@ def load_time_mesurements(file_path): "completion" : [x / 120 for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))] } + +# procceses a single file and appends the desired timings +# to the global data-array, handles multiple runs with a runid +# and ignores if the given file is not found as some +# configurations may not be benchmarked def process_file_to_dataset(file_path, engine_label, thread_count): engine_index = index_from_element(engine_label,engine_counts) engine_nice = engine_counts_nice[engine_index] @@ -69,6 +77,9 @@ def process_file_to_dataset(file_path, engine_label, thread_count): return +# loops over all possible configuration combinations and calls +# process_file_to_dataset for them in order to build a dataframe +# which is then displayed and saved def main(): folder_path = "benchmark-results/" diff --git a/benchmarks/benchmark-plotters/plot-perf-enginelocation.py b/benchmarks/benchmark-plotters/plot-perf-enginelocation.py index c878c95..3fc26dd 100644 --- a/benchmarks/benchmark-plotters/plot-perf-enginelocation.py +++ b/benchmarks/benchmark-plotters/plot-perf-enginelocation.py @@ -1,10 +1,11 @@ import os import json import pandas as pd -from pandas.core.ops import methods import seaborn as sns import matplotlib.pyplot as plt +from common import calc_throughput, index_from_element + runid = "Run ID" x_label = "Copy Type" y_label = "Throughput in GiB/s" @@ -13,24 +14,23 @@ types = ["intersock-n0ton4-1mib", "internode-n0ton1-1mib", "intersock-n0ton4-1gi types_nice = ["Inter-Socket 1MiB", "Inter-Node 1MiB", "Inter-Socket 1GiB", "Inter-Node 1GiB"] copy_methods = ["dstcopy", "srccopy", "xcopy", "srcoutsidercopy", "dstoutsidercopy", "sockoutsidercopy", "nodeoutsidercopy"] copy_methods_nice = [ "Engine on DST-Node", "Engine on SRC-Node", "Cross-Copy / Both Engines", "Engine on SRC-Socket, not SRC-Node", "Engine on DST-Socket, not DST-Node", "Engine on different Socket", "Engine on same Socket"] -title = "Performance of Engine Location - Copy Operation on DDR with 1 Engine per WQ" - -index = [runid, x_label, var_label] -data = [] -def calc_throughput(size_bytes,time_ns): - time_seconds = time_ns * 1e-9 - size_gib = size_bytes / (1024 ** 3) - throughput_gibs = size_gib / time_seconds - return throughput_gibs +title = \ + """Throughput showing impact of Engine Location\n + Copy Operation on DDR with 1 Engine per WQ""" +description = \ + """Throughput showing impact of Engine Location\n + Some Configurations missing as they are not feesible\n + Copy Operation on DDR with 1 Engine per WQ""" -def index_from_element(value,array): - for (idx,val) in enumerate(array): - if val == value: return idx - return 0 +index = [runid, x_label, var_label] +data = [] +# loads the measurements from a given file and processes them +# so that they are normalized, meaning that the timings returned +# are nanoseconds per element transfered def load_time_mesurements(file_path,method_label): with open(file_path, 'r') as file: data = json.load(file) @@ -59,6 +59,11 @@ def load_time_mesurements(file_path,method_label): "completion": data["list"][0]["report"]["time"]["completion"] } + +# procceses a single file and appends the desired timings +# to the global data-array, handles multiple runs with a runid +# and ignores if the given file is not found as some +# configurations may not be benchmarked def create_copy_dataset(file_path, method_label, type_label): method_index = index_from_element(method_label,copy_methods) method_nice = copy_methods_nice[method_index] @@ -80,6 +85,10 @@ def create_copy_dataset(file_path, method_label, type_label): except FileNotFoundError: return + +# loops over all possible configuration combinations and calls +# process_file_to_dataset for them in order to build a dataframe +# which is then displayed and saved def main(): folder_path = "benchmark-results/" diff --git a/benchmarks/benchmark-plotters/plot-perf-peakthroughput.py b/benchmarks/benchmark-plotters/plot-perf-peakthroughput.py index 29957fd..302470b 100644 --- a/benchmarks/benchmark-plotters/plot-perf-peakthroughput.py +++ b/benchmarks/benchmark-plotters/plot-perf-peakthroughput.py @@ -2,34 +2,40 @@ import os import json import pandas as pd from itertools import chain -from pandas.core.ops import methods -from typing import List import seaborn as sns import matplotlib.pyplot as plt +from common import calc_throughput + runid = "Run ID" x_label = "Destination Node" y_label = "Source Node" v_label = "Throughput" -title = "Copy Throughput in GiB/s tested for 1GiB Elements using all 8 DSA Chiplets" + +title_allnodes = \ + """Copy Throughput in GiB/s tested for 1GiB Elements\n + Using all 8 DSA Chiplets available on the System""" +title_smartnodes = \ + """Copy Throughput in GiB/s tested for 1GiB Elements\n + Using Cross-Copy for Intersocket and all 4 Chiplets of Socket for Intrasocket""" + +description_smartnodes = \ + """Copy Throughput in GiB/s tested for 1GiB Elements\n + Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n + Using all 4 DSA Chiplets of a Socket for Intra-Socket Operation\n + And using only the Source and Destination Nodes DSA for Inter-Socket""" +description_allnodes = \ + """Copy Throughput in GiB/s tested for 1GiB Elements\n + Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n + Using all 8 DSA Chiplets available on the System""" index = [ runid, x_label, y_label] data = [] -def calc_throughput(size_bytes,time_ns): - time_seconds = time_ns * 1e-9 - size_gib = size_bytes / (1024 ** 3) - throughput_gibs = size_gib / time_seconds - return throughput_gibs - - -def index_from_element(value,array): - for (idx,val) in enumerate(array): - if val == value: return idx - return 0 - - +# loads the measurements from a given file and processes them +# so that they are normalized, meaning that the timings returned +# are nanoseconds per element transfered def load_time_mesurements(file_path): with open(file_path, 'r') as file: data = json.load(file) @@ -45,6 +51,10 @@ def load_time_mesurements(file_path): } +# procceses a single file and appends the desired timings +# to the global data-array, handles multiple runs with a runid +# and ignores if the given file is not found as some +# configurations may not be benchmarked def process_file_to_dataset(file_path, src_node, dst_node): data_size = 1024*1024*1024 @@ -58,24 +68,31 @@ def process_file_to_dataset(file_path, src_node, dst_node): return -def main(): +# loops over all possible configuration combinations and calls +# process_file_to_dataset for them in order to build a dataframe +# which is then displayed and saved +def main(node_config,title): folder_path = "benchmark-results/" for src_node in range(16): for dst_node in range(16): - file = os.path.join(folder_path, f"copy-n{src_node}ton{dst_node}-1gib-allnodes-1e.json") + file = os.path.join(folder_path, f"copy-n{src_node}ton{dst_node}-1gib-{node_config}-1e.json") process_file_to_dataset(file, src_node, dst_node) df = pd.DataFrame(data) + + data.clear() df.set_index(index, inplace=True) data_pivot = df.pivot_table(index=y_label, columns=x_label, values=v_label) + plt.figure(figsize=(8, 6)) - sns.heatmap(data_pivot, annot=True, cmap="YlGn", fmt=".0f") + sns.heatmap(data_pivot, annot=True, cmap="rocket_r", fmt=".0f") plt.title(title) - plt.savefig(os.path.join(folder_path, "plot-perf-allnodethroughput.png"), bbox_inches='tight') + plt.savefig(os.path.join(folder_path, f"plot-perf-{node_config}-throughput.png"), bbox_inches='tight') plt.show() if __name__ == "__main__": - main() \ No newline at end of file + main("allnodes", title_allnodes) + main("smart", title_smartnodes) \ No newline at end of file diff --git a/benchmarks/benchmark-plotters/plot-perf-submitmethod.py b/benchmarks/benchmark-plotters/plot-perf-submitmethod.py index 4777ad8..672eb34 100644 --- a/benchmarks/benchmark-plotters/plot-perf-submitmethod.py +++ b/benchmarks/benchmark-plotters/plot-perf-submitmethod.py @@ -1,11 +1,11 @@ import os import json import pandas as pd -from pandas.core.ops import methods -from typing import List import seaborn as sns import matplotlib.pyplot as plt +from common import calc_throughput, index_from_element + runid = "Run ID" x_label = "Size of Submitted Task" y_label = "Throughput in GiB/s" @@ -13,25 +13,26 @@ var_label = "Submission Type" sizes = ["1kib", "4kib", "1mib", "32mib"] sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "32 MiB"] types = ["bs10", "bs50", "ms10", "ms50", "ssaw"] -types_nice = ["Batch, Size 10", "Batch, Size 50", "Multi-Submit, Count 10", "Multi-Submit, Count 50", "Single Submit"] -title = "Optimal Submission Method - Copy Operation tested Intra-Node on DDR" - -index = [runid, x_label, var_label] -data = [] +types_nice = ["Batch 10", "Batch 50", "Multi-Submit 10", "Multi-Submit 50", "Single-Submit"] -def calc_throughput(size_bytes,time_ns): - time_seconds = time_ns * 1e-9 - size_gib = size_bytes / (1024 ** 3) - throughput_gibs = size_gib / time_seconds - return throughput_gibs +title = \ + """Throughput showing Optimal Submission Method and Size\n + Copy Operation tested Intra-Node on DDR with 1 Engine per WQ""" +description = \ + """Throughput showing Optimal Submission Method and Size\n + Batch uses a Batch Descriptor of given Size\n + Multi-Submit fills the Work Queue with n Single Descriptors\n + Single-Submit submits one Descriptor and immediately waits\n + Copy Operation tested Intra-Node on DDR with 1 Engine per WQ""" -def index_from_element(value,array): - for (idx,val) in enumerate(array): - if val == value: return idx - return 0 +index = [runid, x_label, var_label] +data = [] +# loads the measurements from a given file and processes them +# so that they are normalized, meaning that the timings returned +# are nanoseconds per element transfered def load_time_mesurements(file_path,type_label): with open(file_path, 'r') as file: data = json.load(file) @@ -54,6 +55,10 @@ def load_time_mesurements(file_path,type_label): } +# procceses a single file and appends the desired timings +# to the global data-array, handles multiple runs with a runid +# and ignores if the given file is not found as some +# configurations may not be benchmarked def process_file_to_dataset(file_path, type_label,size_label): type_index = index_from_element(type_label,types) type_nice = types_nice[type_index] @@ -78,7 +83,9 @@ def process_file_to_dataset(file_path, type_label,size_label): return - +# loops over all possible configuration combinations and calls +# process_file_to_dataset for them in order to build a dataframe +# which is then displayed and saved def main(): folder_path = "benchmark-results/" diff --git a/benchmarks/benchmark-results/plot-opt-submitmethod.png b/benchmarks/benchmark-results/plot-opt-submitmethod.png index 001ae926217778e38dcc6a4f95814e3e2d9fd092..88d20dc4f3036fc3740545fd66a41efa0a7c9ec2 100644 GIT binary patch literal 39576 zcmc$GcQn`U-~XGIQCUTjL`o!*N-`6Qifok;(hwQRUKJS`QKF2p$u4_`C`#Ey*`g#X zvi%-c_kI7)xzByh@0|Ou-}ju)w@;t)e!s?bJ)e*DynN59oY+9Om5xH8Y*0EWuTG&* z;~%PFnzi^ZcP>*c;TLgR1zlSW%geU*MmDCDGe)*n=9aeR*Nk`AncCdEW_d$Y=!npM z!5vp^ZLMw|6&AMmzkfl<(&mcrq3qmLdZW10?; zzrNYKocX)5ZoK_n(UX^#?(@nsOXPi0zVqO-!u1@1mSinIbqSH{Eg5RcX-{|M`7y=C z&W5}=ua&KA&U5UNi<|rGH;wjErQfADczdT^B!AzX5`Q)&KKAOj>yZD)U{6MBdHgS4 zH15tu4<8?&I1p?O(=FMIwg0{1#ow2mEJb1nB{l(EAdj$mOLU=b{U0-ZBD%YHN zrgvaqwU-=~s;a8(@2~7vK1RF7+3~4F`h5PZn`<*T!N|q6qBuHoDuh>FL1FOR*z(fC zmc!;->O%Lf+q-w~=Z+4Z9XlvdM{iaBXil1rw(l-;oqnqQ%<M^ zaDUQyPyXP~j@(qe6I>E@zusn#=I4$Y8yoLXV#YOxj7>E?&d(R)*|~EnEooFlV@p`} zjwhRJDQk9db00Hx_*!87x!hf5X0$U&9rq|6Dd{+Aac`zU+-86#HZG2dot;)fqWI3n z+UG^LM(&M|k5fmiJm5aQ|JF}UPj7FNt`bKpYwOoty zCTvh$6TlH19`2(YDzMo$yiob%Nm*a!UF*AkmpD$X?@AOhH#cWwVY%BFccSX;aC!Nl zhK5GA+mh3!z1r*f`T3o4OHFDwE{<0*%!PQAjo6A`etOu#&(m{tL1E!$s}`>+=ZKT7 z8TuDYKfilNtEi}GHh6FR&kqgV^XIpg=nFXPQhxL{j7!2I&7gFvMv{s`ra`Hu+qRL` zRPT+PM|T`KvIQ?g?&8HAxH!|SIo7?}f`)-C$Iq9$NeSr}dAcsla34IVY5KL|_Nw5h zD1F)BYr^Gi*2nlNMmut=`rh2nwjVR_^zwQ)c)dMCL0g;ke6E>S=w2Pxi=JbA`ho0x ze0)d5#Y;yS>C^wtG(}4vIB;OUaWzdradDh(w(-@^S=>T;1<%#_czIWE7SODCeRrK& zk_soTJr}GO9`Cz9R=#3jy6(e|m%fuZ4yRB1E6>F$-0vA0 zTIVHq+h(w?cj%eB;-yQxC8ec@gsT4hXzuOrUv>TZ_0?Mg(592Ef9A4rp?@>X^v(0_TRsM zpWU+wOXJwxwHqFUhbL>>w`b@lpU)K#6cl7+Wc1i|>TzT|!9 zh46Ck+qZ#yQ~r}ln+J)mnqPj;O${{`SexjcJNNp-hYj7Xe>o{Vcm}gQcNs34ny!(N zkqMn*_b>fBYE7FvKS+=RvD{?kR4Qz_d zi>-R)Li=xgczQ^nKQv4ld(8Xyx8kbCMy5A@o2&5j19Nf?^78TZKU9^z9Tmm-_3PLD z4&xUuUAmNU0Zh^L$3!hwQi?OS2hiT`7=;#bq4HS!P<2Q7V_FZ6o{P=OQUg3c}i_S!AscU^>V?jr6 z{Y>U$IQ3ZY*hpKt(e>-8)#*A9{cP4n)mNv8auVJYr2f z+L`Z-@`JbDGddc8ZPfF#HC15x(LxidU1O?_@Frn{ZGnM-SHBe`=c?oP z*56J|-7C0n-^A;6Ts@z&P0qiGs~4G?Pmmh4zmxcJq*mLYGy5bnuP0;d&7p?Mx;o|l zf~AGO^pwiZ&I>N{Kaz|WW=4&tf40V6dKVIV`pE$vQBjQyj&0in@f1Cil9IwjuLPp* zl^?O{@hG@C6o|scsG_1`{QAxsV=UBI%kDC2kKxwTu#S?MvF=Rf1xw%!=*Pn85bWAxjGCI1ivZ_kK+&s-EAYk+6z1o{*W@h?O zuLMS3zkh%Fk>EM?)8YG%{pu>cfT~Z+!ot#+cJ7s4zZ&(98h`eko{W9-(<8Bl>6W^ucMMZ9W8k- zqrJ@4xp!!YkAX|dNt43J%F1fo`a;XbQC{9N`P{2SNy*Faw@l0%#ao!WFHU&x7ZYO( z70_b7-j>F6A#RAyl{jip7*X=-vjZ9Bjz~z5t9I1w9F2JJ zUNNfD%O@r#jPVZo?hBVOl44$4nYy5(vXM(#km9rTkeSb! zh{L|le}50^=3|iv)ZPzCw4j%DaB}iP@$+p>)w#d#LY~j~xD}Obnqj#?M!xM2bt>7} z*;yYHE#Gg&_V?dCYXapXiYzd_6A>nSM~>*OUbn8k>SMGN3wdhTVa6Rf*Np%E{$`AVRaspv zfACt9{J!0Q5o)h4pUJ-Q?PxZd7!xxy52{@?`su^|qpW@9WBw@`Nq*1V=9N$@@2_Ln zPQz_*!<}M`ogSwZNPN8Yrww1K6`ri4?m7M*$2 z9wi@l^YZ%OF^+6-koOI!B;O| z$`{%UPU;!f$&YnfmVdY

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