keccak: improve perf a little (#2321)

* avoid `burnMem`
* avoid zeroing buffers
* work around `when nimvm` issue

scripts/block-import-stats.py

@@ -14,8 +14,13 @@
 
 def readStats(name: str, min_block_number: int):
  df = pd.read_csv(name).convert_dtypes()
- if df.block_number.iloc[-1] > min_block_number:
- cutoff = min(df.block_number.iloc[-1] - min_block_number, min_block_number)
+ # at least one item - let it lag in the beginning until we reach the min
+ # block number or the table will be empty
+ if df.block_number.iloc[-1] > min_block_number + df.block_number.iloc[0]:
+ cutoff = min(
+ df.block_number.iloc[-1] - min_block_number,
+ min_block_number,
+ )
  df = df[df.block_number >= cutoff]
  df.set_index("block_number", inplace=True)
  df.time /= 1000000000
@@ -43,7 +48,7 @@ def prettySecs(s: float):
 def formatBins(df: pd.DataFrame, bins: int):
  if bins > 0:
  bins = np.linspace(
- df.block_number.iloc[0], df.block_number.iloc[-1], bins, dtype=int
+ df.block_number.iloc[0] - df.blocks.iloc[0], df.block_number.iloc[-1], bins, dtype=int
  )
  return df.groupby(pd.cut(df["block_number"], bins), observed=True)
  else:
@@ -68,15 +73,15 @@ def formatBins(df: pd.DataFrame, bins: int):
 )
 args = parser.parse_args()
 
-baseline = readStats(args.baseline, args.min_block_number)
+baseline = readStats(args.baseline, 0)
 contender = readStats(args.contender, args.min_block_number)
 
 # Pick out the rows to match - a more sophisticated version of this would
 # interpolate, perhaps - also, maybe should check for non-matching block/tx counts
 df = baseline.merge(contender, on=("block_number", "blocks", "txs"))
 
-df["bpsd"] = ((df.bps_y - df.bps_x) / df.bps_x).fillna(0)
-df["tpsd"] = ((df.tps_y - df.tps_x) / df.tps_x).fillna(0)
+df["bpsd"] = ((df.bps_y - df.bps_x) / df.bps_x)
+df["tpsd"] = ((df.tps_y - df.tps_x) / df.tps_x.replace(0, 1))
 df["timed"] = (df.time_y - df.time_x) / df.time_x
 
 df.reset_index(inplace=True)
@@ -131,10 +136,8 @@ def formatBins(df: pd.DataFrame, bins: int):
 time_xt = df.time_x.sum()
 time_yt = df.time_y.sum()
 
-timet = time_yt-df.time_x.sum()
-print(
- f"Time (total): {prettySecs(timet)}, {(timet/time_xt):.2%}"
-)
+timet = time_yt - df.time_x.sum()
+print(f"Time (total): {prettySecs(timet)}, {(timet/time_xt):.2%}")
 
 print()
 print(