Skip to content
vmstats
Branch: master
Clone or download
Latest commit dd77bdf Mar 29, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
4760K_to_5040K initial commit Mar 8, 2019
5050K_to_5410K more metrics Mar 11, 2019
charts
m5d.2xlarge.run2 moar data Mar 29, 2019
m5d.2xlarge.run3 moar data Mar 29, 2019
m5d.2xlarge bar charts for the people Mar 18, 2019
README.md bar charts for the people Mar 18, 2019
main.go moar data Mar 29, 2019

README.md

This repo is a collection of vm statistics, gathered from a geth instance.

Every data point represents 10k blocks. Every time an opcode executed, a timer was stopped, the time since start was noted, and the new opcode timer was started. After 10K blocks, the data was dumped into a json file. The files are available in the /m5.2xlarge/-folder.

The benchmarks are from a m5.2xlarge aws instance that did a full-sync. Such a machine has

  • 8 vcpu,
  • 32 Gb RAM
  • 300 GB NVMe SSD.

Time spent

What the evm spends time on

The large thing there is SLOAD.

If we place a cap on it, and filter so we only see the top four things that take up the execution time, we can see that the top ones are

  • SLOAD, obviously,
  • SSTORE - hardly surprising. Even though SSTORE is expensive, it's also one that touches disk.
  • EXP
  • JUMPI - This is a very common opcode. Also, Geth nowadays defer the jump analysis until it's actually needed - the first time a JUMP/JUMPI is called.

It seems that aside from SLOAD, there's no other single operation that dominates the remaining time that is spent on block processing.

Time Pies!

Let's see how the time spent has progressed through time.

The 2-3M range contains the shanghai attacks.

In the 3M-4M range, we start seeing SLOAD take a larger piece of the pie.

... and accounting for ~50% between 5M and 6M.

Count Pies!

We can also look at the prevalence of opcodes -- that is, how common is an opcode, and how has that varied over time?

Cost of ops

Are operations well-balanced, gas-wise?

Arithmetic ops (0x00-range)

1

The arithmetic ops seems to vary within one order of magnitude -- some peaks which is likely due to noisy data. Here's a graph where it's been capped at 250

1

Comparison ops (0x10-range)

1

The comparisons ops (also capped at 250) are fairly aligned within 35 and 130. The NOT looks pretty over-represented.

SHA3 ops (0x20-range)

The SHA3 operation has it's own range. Since it'a dynamically priced operation, the gas has not been part of the data collection. What we can do, however, is plot the time spent during SHA3, and the number of invocations.

1

The two line up pretty well, so the op doesn't 'degrade' over time. As expected.

Context ops (0x30-range)

The following ops have dynamic gas, and are not charted:

CALLDATALOAD,
CALLDATACOPY,
CODECOPY,
EXTCODECOPY,
RETURNDATACOPY,

Here are the remainders (split up into two charts, both capped at 500): 2 The big spike around 2.08M is, surprisingly, CALLER. Aside from a few spikes, they're fairly well aligned -- except for BALANCE, which is starting to fluctuate and reaching high peaks. It was repriced in EIP150, anad was low for a few million blocks.

Note: BALANCE should really be called EXTBALANCE, since it fetches balance for (potentially) external accounts.

2

The EXTCODESIZE looks extremely cheap -- which makes sense, since it was repriced at block 2463000 from 20 to 700.

The Tangerine Whistle HF also contained these changes:

  • Increase the gas cost of EXTCODESIZE to 700 (from 20).
  • Increase the base gas cost of EXTCODECOPY to 700 (from 20).
  • Increase the gas cost of BALANCE to 400 (from 20).
  • Increase the gas cost of SLOAD to 200 (from 50).
  • Increase the gas cost of CALL, DELEGATECALL, CALLCODE to 700 (from 40).
  • Increase the gas cost of SELFDESTRUCT to 5000 (from 0).

Block operations (0x40 range)

This chart capped at 600.

2

Missing from this chart is BLOCKHASH, which deserves it's own chart. It's plotted along with it's opcode count -- this operation is a bit quirky, and is more expensive when it's executed in isolation. So the more this op is used, the cheaper the operations become (to a degree). This chart capped at 3000.

2

There are some further optimizations that can be done on this op, at least for geth.

Storage and execution (0x50 range)

These are the ops

POP
MLOAD
MSTORE
MSTORE8
SLOAD
SSTORE
JUMP
JUMPI
PC
MSIZE
GAS
JUMPDEST

However,

-MSTORE and MSTORE8 have an additional cost for expanding memory

  • SLOAD varies depending on previous value
  • JUMP/JUMPI have a hidden cost: they require jumpdest analysis.

So the first graph here shows POP, MLOAD, SLOAD,PC, MSIZE and GAS,

2

It's clear that SLOAD went down at EIP150, but has started to rise significantly since then. At around 5M, it was back to the same levels as before EIP150.

0x60 range

These are DUPX,SWAPX and PUSHX. Here capped for spikes, at 600:

Here capped at 100:

0xa0 - Logging

The LOG opcodes are dynamically priced, depending on the memory size, so we don't have gas/time charts, but here's a time spent-chart:

5

0xf0 - calling

Remaining ops are 'special' - both dynamic costs and non-trivial effects, such as starting new call contexts or exiting from call contexts.

CREATE
CALL
CALLCODE
RETURN
DELEGATECALL
CREATE2
STATICCALL
REVERT
SELFDESTRUCT

Some individual OPS

SLOAD

5

BALANCE

5

So what's the most 'heavy'

If 'heavy' means large time per gas unit. Here are some charts, where you can also see the actual cost. For the really cheap opcodes, like PC, those are probably over-represented, since they are so brief that the actual execution is in roughly on the same order of magnitude as actually performing the measurement.

Anyway, some graphs:

You can’t perform that action at this time.