Update analyse_medians.py
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738f753248
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436e336e0c
@ -213,23 +213,29 @@ def ha_dist(core, is_QPI):
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"""
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if is_QPI:
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if core < 4:
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return core
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return 7-core
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return core, 0
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return 7-core, 1 # +1 for PCI
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if core < 4:
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return 3-core
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return core-4
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return 3-core, 0
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return core-4, 0
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def cclockwise_dist(source, dest):
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base = (dest+8-source)%8
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side_jump = 0
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if source < 4 and dest >= 4:
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side_jump = 1
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elif source >= 4 and dest < 4:
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side_jump = 2
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return base, side_jump
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def cclockwise_ha_dist(core, is_QPI):
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"""
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counter-clockwise distance to Home Agent
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"""
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if is_QPI:
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return 7-core
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if core < 4:
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return 3-core
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return 11-core
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return cclockwise_dist(core, 7)
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return cclockwise_dist(core, 3)
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def miss_topology(main_core, slice_group, h, down_jump, top_jump, ini, ha_h):
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core, ring = slice_msg_distance(slice_group, main_core%8)
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@ -237,95 +243,81 @@ def miss_topology(main_core, slice_group, h, down_jump, top_jump, ini, ha_h):
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side_jump = 0
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side_jump += top_jump if ring == 2 else 0
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side_jump += down_jump if ring == 1 else 0
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return (cclockwise_ha_dist(slice_group, False)//2)*ha_h+h*core + side_jump + ini
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return (cclockwise_ha_dist(slice_group, False))*ha_h+h*core + side_jump + ini
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def miss_topology_df(x, h, down_jump, top_jump, ini, ha_h):
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func = lambda x, h, down_jump, top_jump, ini, ha_h: miss_topology(x["main_core_fixed"], x["slice_group"], h, down_jump, top_jump, ini, ha_h)
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return x.apply(func, args=(h, down_jump, top_jump, ini, ha_h), axis=1)
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def remote_hit_topology(main_core, helper_core, slice_group, C, h, H):
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core0, ring0, _ = slice_msg_distance(main_core, slice_group)
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core1, ring1, _ = slice_msg_distance(helper_core, slice_group)
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return C + h*(core0+core1) + H*(ring0+ring1)
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def remote_hit_topology(main_core, helper_core, slice_group, const, core_jump, HA_jump):
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"""
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main_core
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-> local_slice
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-> remote_slice
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-> helper_core
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-> remote_slice
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-> local_slice
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-> main_core
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"""
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if main_core // 8 == helper_core // 8:
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print("Can only do hit predictions for different socket", file=sys.stderr)
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raise NotImplementedError
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def remote_hit_topology_df(x, C, h, H):
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func = lambda x, C, h, H: remote_hit_topology(x["main_core_fixed"], x["helper_core_fixed"], x["slice_group"], C, h, H)
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return x.apply(func, args=(C, h, H), axis=1)
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helper, main = helper_core%8, main_core%8
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main_slice_local = slice_msg_distance(slice_group, main)
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slice_QPI = cclockwise_dist(0, slice_group) # clockwise
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QPI_slice_r = cclockwise_dist(0, slice_group)
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slice_r_helper = slice_msg_distance(slice_group, helper)
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costs = (main_slice_local[0]+slice_QPI[0]+QPI_slice_r[0]+slice_r_helper[0], main_slice_local[1]+slice_QPI[1]+QPI_slice_r[1]+slice_r_helper[1])
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return const+costs[0]*core_jump+costs[1]*HA_jump # may need some adjustments
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def remote_hit_topology_df(x, const, core_jump, HA_jump):
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func = lambda x, const, core_jump, HA_jump: remote_hit_topology(x["main_core_fixed"], x["helper_core_fixed"], x["slice_group"], const, core_jump, HA_jump)
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return x.apply(func, args=(const, core_jump, HA_jump), axis=1)
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def do_predictions(df):
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def plot_predicted_topo(col, row, x_ax, target, pred, df=df):
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title_letter = {
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titles = {
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"main_core_fixed": "A",
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"helper_core_fixed": "V",
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"slice_group": "S"
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}.get(col, col[0])
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"slice_group": "S",
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None: "None"
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}
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figure_A0 = sns.FacetGrid(df, col=col, row=row)
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figure_A0.map(sns.scatterplot, x_ax, pred, color="r")
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figure_A0.map(sns.scatterplot, x_ax, target, color="g", marker="+")
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figure_A0.set_titles(col_template="$"+title_letter+"$ = {col_name}")
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figure_A0.map(sns.scatterplot, x_ax, target, color="g")
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figure_A0.map(sns.scatterplot, x_ax, pred, color="r", marker="+")
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figure_A0.set_titles(
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col_template="$"+titles.get(col, col[0])+"$ = {col_name}",
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row_template="$"+titles.get(row, row[0])+"$ = {row_name}"
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)
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plot(f"medians_{pred}_{col}.png")
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values = []
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main_socket, helper_socket = 0, 0
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dfc = df[(df["main_socket"] == main_socket) & (df["helper_socket"] == helper_socket)]
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cores = sorted(list(dfc["main_core_fixed"].unique()))
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slices = sorted(list(dfc["slice_group"].unique()))
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res_miss = optimize.curve_fit(
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miss_topology_df, dfc[["main_core_fixed", "slice_group"]], dfc["clflush_miss_n"]
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)
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print("Miss topology:")
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print(res_miss)
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values.append(res_miss[0])
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dfc["predicted_miss"] = miss_topology_df(dfc, *(res_miss[0]))
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plot_predicted_topo("slice_group", None, "main_core_fixed", "clflush_miss_n", "predicted_miss", df=dfc)
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plot_predicted_topo("main_core_fixed", None, "slice_group", "clflush_miss_n", "predicted_miss", df=dfc)
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for slice_ in slices:
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dfc = df[(df["slice_group"] == slice_) & (df["main_socket"] == main_socket) & (df["helper_socket"] == helper_socket)]
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res_miss = optimize.curve_fit(
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miss_topology_df, dfc[["main_core_fixed", "slice_group"]], dfc["clflush_miss_n"]
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)
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values.append(res_miss[0])
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dfc[f"predicted_miss_{slice_}"] = miss_topology_df(dfc, *(res_miss[0]))
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plot_predicted_topo("slice_group", None, "main_core_fixed", "clflush_miss_n", f"predicted_miss_{slice_}", df=dfc)
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print(list(values[0]))
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print()
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for i in values[1:]:
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print(list(i))
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values = []
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for core in cores:
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dfc = df[(df["main_core_fixed"] == core) & (df["main_socket"] == main_socket) & (df["helper_socket"] == helper_socket)]
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res_miss = optimize.curve_fit(
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miss_topology_df, dfc[["main_core_fixed", "slice_group"]], dfc["clflush_miss_n"]
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)
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values.append(res_miss[0])
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dfc[f"predicted_miss_core{core}"] = miss_topology_df(dfc, *(res_miss[0]))
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plot_predicted_topo("main_core_fixed", None, "slice_group", "clflush_miss_n", f"predicted_miss_core{core}", df=dfc)
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for i in values:
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print(list(i))
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return
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main_socket, helper_socket = 0, 1
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dfc = df[(df["main_socket"] == main_socket) & (df["helper_socket"] == helper_socket)]
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res_remote_hit = optimize.curve_fit(
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remote_hit_topology_df, df[["main_core_fixed", "helper_core_fixed", "slice_group"]], df["clflush_remote_hit"]
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remote_hit_topology_df, dfc[["main_core_fixed", "helper_core_fixed", "slice_group"]], dfc["clflush_remote_hit"]
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)
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print("Remote hit topology:")
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print(res_remote_hit)
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df["diff_miss"] = df["clflush_miss_n"] - df["predicted_miss"]
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facet_grid(
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df, None, "main_core_fixed", "slice_group",
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@ -339,10 +331,20 @@ def do_predictions(df):
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shown=["diff_miss"],
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separate_hthreads=True
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)
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dfc["predicted_remote_hit"] = remote_hit_topology_df(dfc, *(res_remote_hit[0]))
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plot_predicted_topo("slice_group", "helper_core_fixed", "main_core_fixed", "clflush_remote_hit", "predicted_remote_hit", df=dfc)
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plot_predicted_topo("main_core_fixed", "slice_group", "helper_core_fixed", "clflush_remote_hit", "predicted_remote_hit", df=dfc)
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plot_predicted_topo("helper_core_fixed", "main_core_fixed", "slice_group", "clflush_remote_hit", "predicted_remote_hit", df=dfc)
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# df["predicted_remote_hit"] = remote_hit_topology_df(df, *(res_remote_hit[0]))
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# plot_predicted_topo("slice_group", "helper_core_fixed", "main_core_fixed", "clflush_remote_hit", "predicted_remote_hit")
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# plot_predicted_topo("main_core_fixed", "helper_core_fixed", "slice_group", "clflush_remote_hit", "predicted_remote_hit")
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for col in ["slice_group", "helper_core_fixed", "main_core_fixed"]:
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for val in sorted(list(dfc[col].unique())):
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df_temp = dfc[(dfc[col] == val)]
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res_remote_hit = optimize.curve_fit(
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remote_hit_topology_df, df_temp[["main_core_fixed", "helper_core_fixed", "slice_group"]], df_temp["clflush_remote_hit"]
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)
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df_temp[f"predicted_remote_hit_{col}={val}"] = remote_hit_topology_df(df_temp, *(res_remote_hit[0]))
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plot_predicted_topo("slice_group", "helper_core_fixed", "main_core_fixed", "clflush_remote_hit", f"predicted_remote_hit_{col}={val}", df=df_temp)
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plot_predicted_topo("main_core_fixed", "helper_core_fixed", "slice_group", "clflush_remote_hit", f"predicted_remote_hit_{col}={val}", df=df_temp)
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@ -374,6 +376,7 @@ def facet_grid(
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colors=["y", "r", "g", "b"],
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separate_hthreads=False,
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title=None,
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letters=None
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):
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"""
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Creates a facet grid showing all points
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@ -400,6 +403,10 @@ def facet_grid(
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else:
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grid.map(draw_fn, third, el, color=colors[i % len(colors)], marker='+')
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if letters is not None:
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grid.set_titles(col_template="$"+letters[0]+"$ = {row_name}", row_template="$"+letters[1]+"$ = {col_name}")
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if title is not None:
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plot(title, g=grid)
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return grid
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@ -465,27 +472,66 @@ if args.rslice:
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rslice()
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do_predictions(stats)
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#all_facets(stats, shown=["clflush_remote_hit"], colors=["r"], pre="hit")
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#all_facets(stats, shown=["clflush_miss_n"], colors=["b"], pre="miss")
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all_facets(stats, shown=["clflush_remote_hit"], colors=["r"], pre="hit_")
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all_facets(stats, shown=["clflush_miss_n"], colors=["b"], pre="miss_")
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#df=stats
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#for m, h, s in itertools.product((0, 1), (0, 1), df["slice_group"].unique()):
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# dfc = df[(df["main_socket"] == m) & (df["main_core_fixed"]%8é == s) & (df["helper_socket"] == h)]
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#
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# grid = sns.FacetGrid(dfc, row=None, col=None)
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# grid.map(sns.scatterplot, "slice_group", "clflush_miss_n", marker="+")
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#
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# plot(f"miss_m{m}h{h}m{s}", g=grid)
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def compare_facing():
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df=stats
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for m, h, s in itertools.product((0, 1), (0, 1), df["slice_group"].unique()):
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dfc = df[(df["main_socket"] == m) & (df["main_core_fixed"]%8 == s) & (df["helper_socket"] == h)]
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grid = sns.FacetGrid(dfc, row=None, col=None)
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grid.map(sns.scatterplot, "slice_group", "clflush_miss_n", marker="+")
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plot(f"miss_m{m}h{h}m{s}", g=grid)
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#with Pool(8) as pool:
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# pool.starmap(
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# do_facet,
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# itertools.product(
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# stats["main_socket"].unique(),
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# stats["helper_socket"].unique(),
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# (False, ),
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# ("hit", "miss")
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# )
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# )
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def isolate_sockets():
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with Pool(8) as pool:
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pool.starmap(
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do_facet,
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itertools.product(
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stats["main_socket"].unique(),
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stats["helper_socket"].unique(),
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(False, ),
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("hit", "miss")
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)
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)
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def superpose_sockets():
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for main, same_socket in itertools.product(sorted(stats["main_core_fixed"].unique()), (True, False)):
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df = stats[
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(stats["slice_group"] == (main%8))
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& (stats["main_core_fixed"] == main)
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& ((stats["main_socket"] == stats["helper_socket"]) == same_socket)
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]
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ax = sns.scatterplot(df, x="helper_core_fixed", y="clflush_remote_hit", marker="+", color="r")
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ax.set_title(f"$S = {main%8}, V = {main}$")
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plot(f"hit_{same_socket}_main{main:02d}.png")
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df = stats[
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(stats["slice_group"] == (stats["main_core_fixed"]%8))
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& ((stats["main_core_fixed"]%8) == (stats["helper_core_fixed"]%8))
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& (stats["main_socket"] != stats["helper_socket"])
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]
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ax = sns.scatterplot(df, x="slice_group", y="clflush_remote_hit", marker="+", color="r")
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plot(f"hit_same_slice.png")
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stats["main_core_nosock"] = stats["main_core_fixed"]%8
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stats["helper_core_nosock"] = stats["helper_core_fixed"]%8
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facet_grid(
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stats[(stats["main_socket"] != stats["helper_socket"])], "helper_core_nosock", "main_core_nosock", "slice_group",
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title=f"hit_facet_slice_diff_socket.png",
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separate_hthreads=True,
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shown=["clflush_remote_hit"],
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letters="VA"
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)
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facet_grid(
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stats[(stats["main_socket"] == stats["helper_socket"])], "helper_core_nosock", "main_core_nosock", "slice_group",
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title=f"hit_facet_slice_same_socket.png",
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separate_hthreads=True,
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letters="VA",
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shown=["clflush_remote_hit"]
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)
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