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    • Francisco Aguirre's avatar
      XCMv4 (#1230) · 8428f678
      Francisco Aguirre authored 
      

# Note for reviewer

Most changes are just syntax changes necessary for the new version.
Most important files should be the ones under the `xcm` folder.

# Description 

Added XCMv4.

## Removed `Multi` prefix
The following types have been renamed:
- MultiLocation -> Location
- MultiAsset -> Asset
- MultiAssets -> Assets
- InteriorMultiLocation -> InteriorLocation
- MultiAssetFilter -> AssetFilter
- VersionedMultiAsset -> VersionedAsset
- WildMultiAsset -> WildAsset
- VersionedMultiLocation -> VersionedLocation

In order to fix a name conflict, the `Assets` in `xcm-executor` were
renamed to `HoldingAssets`, as they represent assets in holding.

## Removed `Abstract` asset id

It was not being used anywhere and this simplifies the code.

Now assets are just constructed as follows:

```rust
let asset: Asset = (AssetId(Location::new(1, Here)), 100u128).into();
```

No need for specifying `Concrete` anymore.

## Outcome is now a named fields struct

Instead of

```rust
pub enum Outcome {
  Complete(Weight),
  Incomplete(Weight, Error),
  Error(Error),
}
```

we now have

```rust
pub enum Outcome {
  Complete { used: Weight },
  Incomplete { used: Weight, error: Error },
  Error { error: Error },
}
```

## Added Reanchorable trait

Now both locations and assets implement this trait, making it easier to
reanchor both.

## New syntax for building locations and junctions

Now junctions are built using the following methods:

```rust
let location = Location {
    parents: 1,
    interior: [Parachain(1000), PalletInstance(50), GeneralIndex(1984)].into()
};
```

or

```rust
let location = Location::new(1, [Parachain(1000), PalletInstance(50), GeneralIndex(1984)]);
```

And they are matched like so:

```rust
match location.unpack() {
  (1, [Parachain(id)]) => ...
  (0, Here) => ...,
  (1, [_]) => ...,
}
```

This syntax is mandatory in v4, and has been also implemented for v2 and
v3 for easier migration.

This was needed to make all sizes smaller.

# TODO
- [x] Scaffold v4
- [x] Port github.com/paritytech/polkadot/pull/7236
- [x] Remove `Multi` prefix
- [x] Remove `Abstract` asset id

---------

Co-authored-by: command-bot <>
Co-authored-by: default avatarKeith Yeung <[email protected]>
      8428f678
    • Andrei Eres's avatar
      subsystem-bench: cache misses profiling (#2893) · ec7bfae0
      Andrei Eres authored 
      ## Why we need it
To provide another level of understanding to why polkadot's subsystems
may perform slower than expected. Cache misses occur when processing
large amounts of data, such as during availability recovery.

## Why Cachegrind
Cachegrind has many drawbacks: it is slow, it uses its own cache
simulation, which is very basic. But unlike `perf`, which is a great
tool, Cachegrind can run in a virtual machine. This means we can easily
run it in remote installations and even use it in CI/CD to catch
possible regressions.

Why Cachegrind and not Callgrind, another part of Valgrind? It is simply
empirically proven that profiling runs faster with Cachegrind.

## First results
First results have been obtained while testing of the approach. Here is
an example.

```
$ target/testnet/subsystem-bench --n-cores 10 --cache-misses data-availability-read
$ cat cachegrind_report.txt
I refs:        64,622,081,485
I1  misses:         3,018,168
LLi misses:           437,654
I1  miss rate:           0.00%
LLi miss rate:           0.00%

D refs:        12,161,833,115  (9,868,356,364 rd   + 2,293,476,751 wr)
D1  misses:       167,940,701  (   71,060,073 rd   +    96,880,628 wr)
LLd misses:        33,550,018  (   16,685,853 rd   +    16,864,165 wr)
D1  miss rate:            1.4% (          0.7%     +           4.2%  )
LLd miss rate:            0.3% (          0.2%     +           0.7%  )

LL refs:          170,958,869  (   74,078,241 rd   +    96,880,628 wr)
LL misses:         33,987,672  (   17,123,507 rd   +    16,864,165 wr)
LL miss rate:             0.0% (          0.0%     +           0.7%  )
```

The CLI output shows that 1.4% of the L1 data cache missed, which is not
so bad, given that the last-level cache had that data most of the time
missing only 0.3%. Instruction data of the L1 has 0.00% misses of the
time. Looking at an output file with `cg_annotate` shows that most of
the misses occur during reed-solomon, which is expected.
      ec7bfae0