# đī¸Protocol Optimizations

### Introduction

We present gas and reserve measurements for low-level design optimizations of the reference TWAMM design [3], including a significant TWAMM-specific storage optimization alluded to in [2]. It concludes with a comparison to FRAXâs current mainnet TWAMM implementation [4].

The results of combining the TWAMM approximation discussed in [2] and the TWAMM-specific storage optimization quantified herein, offer gas savings of nearly 25% on average and over 40% for some peak measures.

### Optimizations

It is shown in [2] that optimal computational efficiency for TWAMM is when the FRAX approximation is used over the original Paradigm TWAMM algorithm. Thus the original reference TWAMM design [3] was modified to use the FRAX approximation, greatly simplifying arithmetic and allowing the removal of PRB Math [5] fromInt and toInt scaling calls in much of the design. This modified design is referred to throughout this article as the *updated reference design*.

Eight phases of gas reduction optimizations were then applied to the updated reference design:

**Optimization 1**This alters the numerical range and resolution of variables in the design, notably the reserves, from the properties of the PRBMathUD60x18 to the UQ112.112 number format shown in Table 1 below:

**Optimization 2**Replace multiplication and division with arithmetic shifts where possible.

**Optimization 3**TWAMM-specific storage optimization.

This optimization significantly reduces TWAMMâs storage of state for every block interval. Gas savings increase as the number of intervals between calls implying virtual order execution increases.

This imposes new restrictions in specific contexts, requiring numerical analysis and tuning to be presented in a forthcoming article.

**Optimization 4**Bit-packing of swap-direction and order ID into one 256-bit data word.

This reduces the maximum order ID from 2^256 - 1 to 2^255 - 1.

**Optimization 5**Replace the reserve value map with a reserve struct.

Refactor optimization #3 operations into functions.

**Optimization 6**Bit-packing of Order Block Interval (OBI) with reserves.

Remove redundant OBI and token address storage from LongTermOrdersLib.

This limits OBI from 1 to 65535 blocks.

**Optimization 7**Convert LongTermOrder and OrderPool from âstorageâ to âmemoryâ in execute virtual orders loop.

**Optimization 8**Remove unnecessary type casts from optimization #3.

Numerous additional optimization possibilities were explored, but measurements revealed that they degraded gas performance unacceptably.

#### Gas Measurements

Tables 2, 3, and 4 below present the measured minimum, average, and maximum gas usage, respectively, for designs implementing each of the numbered optimizations, applied incrementally (i.e. the optimization phase 3 design also contains optimizations 1 and 2). The incremental gas savings is presented between phases (for phase 1, the updated reference design is the comparison baseline).

The gas measurement methodology is described in Appendix A; it has been updated to correct an error discovered in [2]. The benchmark design configuration is identical to the one presented in Appendix B of [2]. Contracts compiled with Solidity Compiler 0.8.10 and pool OBI set to 10.

Tables 5, 6, and 7 below show **net** gas savings (instead of incremental savings shown in tables 2-4) of the updated and optimized designs compared to the original reference design. Comparing the measurements of the original and updated reference design shows the effect of removing scaling calls and applying the TWAMM algorithm approximation.

Table 8 shows the total gas used for all design method calls and presents the savings as a percentage of the gas reduced from that used by the original reference design:

**A significant improvement in gas usage is demonstrated in the results of tables 2-4, with average savings approaching 25% as shown in table 8.** The largest improvements occur in the maximum measured gas figures for each function. Optimization phase 3, the TWAMM-specific storage optimization, contributes the largest improvements to average and maximum measured figures, followed by the order ID bit-packing of optimization 4.

The measured figures heavily depend on stimulus from the benchmark test configuration, specifically the Long Term (LT) Swap scenario of the TWAMM Pool (see [1] for definition and explanation), blocks of pool inactivity prior to calling contract functions, and the OBI value. As presented in [1], pool inactivity and OBI directly impact the execution of virtual orders, which all methods undergoing measurement depends upon.

Different benchmark configurations will yield different results and can be manipulated to present advantageous figures. The benchmark configuration used in this test tries to approximate reasonably expected market activity in a 2000 block period while exercising all contract functions. While real-world activity will differ, the results here are sufficient to demonstrate that the optimizations can be expected to generally improve gas use with some minor exceptions (see phases 5 and 7 of table 3).

Optimizations 3 and 7 offer increased gas savings with increasing pool inactivity, however, optimization 3 is still beneficial when this is not the case--this is borne out by its improved savings figures for minimum measurements not seen with optimization 7. An improved understanding of realistic market activity for TWAMM will help drive future decisions around optimization phase 7, which the data shows to be detrimental for minimum measured gas use, specifically the regular swap and withdraw methods. At this juncture, the improvements seen in the average and maximum measurements for these methods and this optimization have resulted in optimization 7 being retained.

The total gas use figures presented in Table 8 show that the optimized design reduces gas use by nearly 25% on average, largely due to optimizing data stored on chain by the TWAMM algorithm. This differs from the FRAX TWAMM algorithm approximation, which reduces gas use by optimizing computational efficiencies.

#### Numerical Measurements

As noted for optimization 3, detailed numerical analysis, tuning, and handling of limitations are required prior to deploying this solution. However, a comparison of the difference between the measured reserves for the benchmark test can be used to quickly gauge any egregious effects on the behavior of the pools resulting from the optimizations.

Figure 1 shows the optimized design more closely tracks the original reference design until the simulation block offset 900. The differences in results for both the updated and optimized designs likely arise from the untuned use of the TWAMM algorithm approximation. However, the optimized design likely achieves a closer result until block offset 900 because of the higher fractional fidelity of the UQ112.112 number format it uses (see resolution comparison in Table 1). Between block offset 900 and 1750, the optimized design differences become larger than the updated reference design, improving again after block 1750. This is likely due to optimization 3 and will require further numerical analysis and tuning. Additionally, it should be noted that the updated reference design results of Figure 1 suggest that numerical analysis and tuning of the TWAMM algorithm approximation are also required.

Figure 2 plots the price of pool token B in terms of token A, providing a more intuitive perspective of the numerical effects of the optimized design. The optimized deviates more than the updated reference design between block offset 950 until around 1700 when it begins to more closely follow the original reference design.

Further analysis and tuning are needed with a longer simulation that showcases the long-term effects of any errors introduced. Unlike a closed system, arbitrage will likely correct the error introduced, however, it is important to minimize this error to preserve the advantages of TWAMM over a Constant Product Automated Market Maker (CPAMM) for large swaps and in an environment with insufficient arbitrage.

### Mainnet FRAX TWAMM Comparison

The comparisons in the previous section are academic as none of the contracts are live on mainnet. Fortunately, FRAX has deployed a TWAMM contract on mainnet that can be compared against.

#### FRAX TWAMM Contract Differences

The FRAX TWAMM contract [4] significantly differs from the other designs discussed in this article, particularly in the following respects:

It does not use block intervals but instead uses time intervals of 3600s, 1 hour. Assuming average Ethereum block times of 14s, the approximate OBI is 257.

It uses the UQ112.112 number format but truncates the 112 fractional bits to efficiently store two reserve values in a single 256-bit data word.

It is built atop a Uniswap V2 Automated Market Maker (AMM) [7], implementing ancillary functionality of that AMM, not featured in the designs discussed in this article. For example, it implements a Time Weighted Average Price (TWAP) feature.

The interaction model for providing liquidity and swaps differs from the designs discussed in this article, requiring users or interfacing software and contracts to perform transfers and then invoke contract methods.

These design differences will result in significant gas use differences. The purpose of any comparison is to get a sense of where the optimized design herein stands relative to a live contract regarding gas usage and numerical accuracy.

#### Benchmark Test Setup

The benchmark test was modified to operate on the original reference and optimized designs with their OBI set to 257, accounting for the 3600s virtual order execution interval used by the mainnet FRAX contract.

Additionally, the benchmark test driver was adapted to work with the FRAX TWAMM contracts with differing function names, signatures, and use models.

#### Gas Measurements

The table below shows the optimized design gas use is on the order of the FRAX TWAMM contract, consuming 20% - 50% less gas in the benchmark test.

*Disclaimer: It is important to note that these measurements in no way take away from the FRAX TWAMM implementation; as noted it is performing operations beyond those TWAMM operations being measured implicitly, (i.e. TWAP oracle, etc.).*

The optimized design gas usage measured will likely increase as it is readied for production, due to the planned contract modifications in the below section on future work.

#### Numerical Measurements

Figures 3 and 4 compare the FRAX design and optimized design reserves and token B price, respectively, to the original reference design with OBI set to 257.

The figures reveal that the FRAX contract tracks the behavior of the original reference design more closely, especially after block 800. Notably, the FRAX implementation introduces additional scaling that might be related to the differences seen in the updated reference and optimized designs from the original reference design. These comparisons highlight the need for analysis of the numerical performance and accuracy of the optimized design in the coming development cycles.

### Conclusion

Design optimizations beyond the FRAX TWAMM algorithm approximation were applied to the reference design and demonstrated a significant reduction in gas use, averaging gas savings of nearly 25%.

A comparison with the production FRAX TWAMM contract showed that while gas use of the optimized design was favorable, additional work on numerical accuracy and performance is required to ensure the optimized design tracks the reference TWAMM implementation more closely, ensuring the slippage advantage over CPAMM is maximized.

### Future Work

Numerical analysis and tuning of the TWAMM-specific storage optimization and TWAMM approximation algorithm.

Adapting the design to use an underlying Balancer Vault CPAMM.

Conversion of the system from block-based intervals to time-based intervals will be considered, exchanging increased design complexity and possible gas usage for the following benefits:

Cross-chain compatibility

Intuitive contract interface (humans think in time, not blocks).

### References

âTime Weighted Average Market Maker Operational Parameters vs. Gas Usage Analysisâ, March 2022. Online. Available: mirror.xyz/0slippage.eth/5zKJW4Zx9zYHpB4jNln16HuU8d8EtawmA17usNfIje4.

âTWAMM Algorithm Optimization & Approximation Analysisâ, April 2022. Online. Available: mirror.xyz/0slippage.eth/5zKJW4Zx9zYHpB4jNln16HuU8d8EtawmA17usNfIje4.

TWAMM (2021). Online. Available: github.com/FrankieIsLost/TWAMM.

frax-solidity Uniswap_V2_TWAMM (2022). Online. Available: github.com/FraxFinance/frax-solidity/tree/master/src/hardhat/contracts/Uniswap_V2_TWAMM.

Paul R. Berg, PRBMath (2021). Online. Available: github.com/paulrberg/prb-math.

Q (number format) (2022). Online. Available: en.wikipedia.org/wiki/Q_(number_format).

H. Adams, N. Zinsmeister, and D. Robinson, âUniswap v2 Coreâ, March 2020. Online. Available: uniswap.org/whitepaper.pdf.

### Appendix A: Gas Measurement Methodology

Two measurement methodologies are presented in [2], a high-level and isolating methodology.

Unfortunately, The high-level methodology suffered from a bug where the gas from transfers and approvals related to a transaction were each counted as the type of transaction. For example for âiâ swap transactions, the average gas computed would be:

One way to correct the bug would be to remove the multiplication by 3 in the denominator; this would result in the average gas for a swap including the gas used for related transfers and approvals. However, the high-level measurements in this post exclude the transfer and swap gas use to isolate the swap call gas usage:

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