Merged main

blst/Cargo.toml

@@ -4,15 +4,14 @@ version = "0.1.0"
 edition = "2021"
 
 [dependencies]
-# TODO: Use `std` feature once https://github.com/supranational/blst/pull/150 or similar upstreamed
 blst = { git = 'https://github.com/supranational/blst.git' }
 kzg = { path = "../kzg", default-features = false }
 libc = { version = "0.2.148", default-features = false }
 once_cell = { version = "1.18.0", features = ["critical-section"], default-features = false }
 rand = { version = "0.8.5", optional = true }
 rayon = { version = "1.8.0", optional = true } 
 smallvec = { version = "1.11.1", features = ["const_generics"] }
-hex = "0.4.3"
+hex = { version = "0.4.3", default-features = false, features = ["alloc"] }
 
 [dev-dependencies]
 criterion = "0.5.1"
@@ -23,9 +22,9 @@ rand = "0.8.5"
 default = [
     "std",
     "rand",
-    "blst-default"
 ]
 std = [
+    "hex/std",
     "kzg/std",
     "libc/std",
     "once_cell/std",
@@ -39,18 +38,6 @@ parallel = [
     "kzg/parallel"
 ]
 
-# By default, compile with ADX extension if the host supports it.
-# Binary can be executed on systems similar to the host.
-blst-default = ["blst/default"]
-
-# Compile in portable mode, without ISA extensions.
-# Binary can be executed on all systems.
-blst-portable = ["blst/portable"]
-
-# Enable ADX even if the host CPU doesn't support it.
-# Binary can be executed on Broadwell+ and Ryzen+ systems.
-blst-force-adx = ["blst/force-adx"]
-
 [[bench]]
 name = "das"
 harness = false

tasks/II.md

@@ -0,0 +1,24 @@
+Task II - Optimise MSM
+
+* The codebase needs to be updated to the latest [c-kzg-4844](https://github.com/ethereum/c-kzg-4844), dependencies also needs to be upgraded;
+* KZG10 does a heavy MSM computation when calculating commitment and opening. `rust-kzg` with `blst` backend uses Pippenger method (including the parallel version of it) and produces decent results, however, `go-kzg-4844` is faster here, so there is a place for improvements. Possible optimisation directions:
+  - BGMW algoritm ([this dissertation](https://uwspace.uwaterloo.ca/bitstream/handle/10012/19626/Luo_Guiwen.pdf?sequence=3) is an easy to read source). We will likely benefit from it because our trusted setup is not very large, we can store precomputations in the memory. C++ implementation can be found [here](https://github.com/LuoGuiwen/MSM_blst/blob/2e098f09f07969ac3191406976be6d1c197100f2/main_p1.cpp#L294). However, we need a parallelized Rust version, so it could be that the best way is to build on top of [blst parallel implementation](https://github.com/supranational/blst/blob/master/bindings/rust/src/pippenger.rs#L116). Feel free to explore other code bases and use it if there is actually a more convenient parallel Pippenger implementation in Rust.
+  - Other optimisations proposed in that [this dissertation](https://uwspace.uwaterloo.ca/bitstream/handle/10012/19626/Luo_Guiwen.pdf?sequence=3);
+  - Optimisations implemented in [arkmsm](https://github.com/snarkify/arkmsm), they also have an [explanation](https://hackmd.io/@drouyang/msm);
+  - Cuda GPU parallel algorithms, some examples [here](https://github.com/z-prize/2022-entries/tree/main/open-division/prize1-msm/prize1a-msm-gpu), but very likely there are faster versions now;
+  - Discuss other ideas with the supervisor.
+* Teams will need to try to figure out a way to get their algorithms useful for other ECC backends, if this is not possible for some reason - MSM optimistations will be needed to be implemented for `blst` backend;  
+* The build must pass on Github CI.
+
+Points: 2
+
+Deadline 2023-11-09
+
+----------------------------------------------------------------
+
+Each task has its deadline specified in the task that allows getting 100% of the points if done correctly. However, each late week significantly reduces the points:
+
+1 week -25%
+2 week -50%
+3 week -75%
+4 week -100%