A blockchain is a distributed data structure that implements a ledger: a tamper-proof, widely-accessible, append-only sequence of transactions. Blockchains form the basis for cryptocurrencies [8, 10, 11, 12, 13] and other applications that must maintain a shared state in the absence of a trusted central authority. In the Ethereum  blockchain, for example, if Alice wants to send a coin to Bob, she broadcasts her transaction to one or more miners, who package transactions into blocks, and then undertake a consensus protocol to agree on which block should be appended next to the shared blockchain. A validator is any party who reads the blockchain state and checks it for correctness. Miners are validators, of course, but so is any party who needs to query the blockchain state.
In many blockchain systems, client transactions can invoke scripts, often called smart contracts, or just contracts, that perform logic needed to support complex services such as trading, voting, and managing tokens. Here, we focus on Ethereum-style smart contracts.
Ethereum’s smart contracts present a concurrency challenge. To reconstruct the blockchain’s current state, each validator must re-execute, in a sequential, one-at-a-time order, every call to every smart contract. Such sequential validation is unattractive because it fails to exploit the concurrency provided by modern multicore architectures. Simply executing those calls in parallel is unsafe, because there may be dependencies between contracts: if one contract depends on the results of another, then those contracts must be executed in the same order by every validator. Because the Ethereum smart contract language is Turing-complete, and because contracts can reference one another through untyped function pointers, static analysis is unlikely to be broadly effective.
The most promising approach to concurrent execution of smart contracts is speculation [1, 4, 7]: the virtual machine executes contract calls in parallel against the current state, tracking each transaction’s read set and write set (memory locations read and written). Writes to memory are intercepted and buffered. Two transactions conflict if they access the same memory location, and one access is a write. For every pair of conflicting transactions, one is discarded, and the other is committed. Speculative techniques typically work well when conflicts are rare, but perform poorly when conflicts are common.
How well does speculative concurrency work for smart contract execution? This paper makes the following contributions. We exploit publicly-available historical data to estimate conflict rates in an existing blockchain. This methodology, replaying the historical transaction record against proposed alternative run-times, could be a useful model for other blockchain-centered investigations. This study is exploratory: it aspires to provide a relatively fast and cheap estimate of how well certain strategies are likely to do in practice, with the goal of focusing future research attention in directions more likely to be productive. Of course, an exploratory study necessarily employs sampling, estimation, and approximation.
As described below in more detail, we re-execute blocks sampled from the Ethereum blockchain against a simple speculative execution engine. This engine has two phases: in the first (concurrent) phase, all transactions are run in parallel. In the second (sequential) phase, transactions observed to conflict are discarded and re-run in one-at-a-time order. This execution strategy produced speed-ups ranging from about 8-fold for blocks sampled from 2016, gradually declining to about 2-fold for blocks sampled from 2017.
This study makes the following observations.
Even simple speculative strategies yield non-trivial speed-ups.
Over time, however, these speed-ups declined as transaction traffic increased.
Distinguishing between reads and writes is important: treating a transaction’s read and write sets as a single conflict set substantially increases conflict rates.
More aggressive speculative strategies, such as running multiple concurrent phases, yield little additional benefit.
Accurate static conflict analysis may yield a modest benefit.
Increasing the number of cores in the simulated virtual machine from 16 to 64 improved speed-ups, but there was little improvement above 64 cores.
In high-contention periods, most contention resulted from a very small number of popular contracts.
These observations suggest some directions for further research.
In periods of high contention, most conflict is caused by a small number of very popular contracts. Today, contract writers have no motivation for avoiding such conflicts. It could be productive to devise incentives, perhaps in the form of reduced gas prices, for contracts that produce fewer data conflicts.
Many data conflicts, such as crediting and debiting account balances, are probably artifacts of defining conflict naïvely in terms of read-write sets. Perhaps conflicts could be reduced by extending the virtual machine to provide explicit support for common commutative operations such as credits and debits.
1.2 Methodology in Brief
We replay each transaction in a block, computing each transaction’s read and write sets. We then greedily sort the transactions into two bins: the concurrent bin holds transactions that do not conflict with any other transaction already in the concurrent bin, and the sequential bin holds the rest.
We then estimate the elapsed time required to (1) execute the concurrent bin transactions in parallel (including the cost of detecting and discarding conflicting transactions), followed by (2) sequentially executing the sequential bin transactions.
Since we do not have a parallel EVM implementation to test, we estimate a transaction’s running time in two ways: either by the gas it consumed, or by the number of Ethereum Virtual Machine (EVM) bytecode instructions executed. (Both measures are easy to compute, and yield similar results.) The speed-up is the ratio between the estimated elapsed times for the sequential executions versus the longest speculative execution.
In the next section we describe related work. In Section 3, we outline Ethereum’s architecture, smart contracts, and all relevant terminology. Section 4 describes the setup of our empirical study along with statistics summarizing observed results, while in Section 5, we consider various alternatives to the baseline setup. Finally, in Sections 6 and 7, we discuss conclusions and potential future directions for extending this work.
2 Related Work
Smart contracts were first proposed by Szabo .
Bitcoin  includes a scripting language of limited power. Ethereum  is perhaps the most widely used smart contract platform, running on a quasi-Turing-complete virtual machine. Solidity  is the most popular programming language for the Ethereum virtual machine. Other blockchains that support smart contracts include Corda  and Cardano .
Hyperledger Fabric  is a permissioned blockchain where transactions (calls to smart contracts) are executed speculatively in parallel against the latest committed state. Transactions’ read and write sets are recorded and compared, and conflicting contracts are discarded.
3 Ethereum Smart Contracts
In this section, we sketch Ethereum smart contracts, mining, and validation of Ethereum blocks.
3.1 The Architecture
In Ethereum, as in other blockchains, multiple nodes follow a common protocol in which transactions from clients are packaged into blocks, and nodes use a consensus protocol to agree on successive blocks. Each block includes a cryptographic hash of its predecessor, making it difficult to tamper with the ledger.
Each client has ownership of one or more accounts, so that each transaction occurs between a sender account and a recipient account. The majority of transactions are one of two kinds: either a value transfer, which is a purely monetary transfer of ether from sender to recipient, or a contract call, where the sender account makes a call to code associated with the recipient account.
Some Ethereum accounts, in addition to maintaining a balance of ether, possess associated code called a smart contract. A smart contract resembles an object in a programming language, with a long-lived state recorded in the blockchain. This state is manipulated by a set of functions called either directly by clients (top-level calls) or indirectly by other smart contracts (internal calls). To ensure that function calls terminate, each computational step incurs a cost in gas, paid by the caller. The caller specifies a maximum amount of gas it is willing to pay, and if the charge exceeds that sum, the computation is terminated and rolled back, and the caller’s gas is not refunded. Nevertheless, the rolled-back transaction is still recorded on the blockchain.
A smart contract’s code consists of a sequence of bytecode instructions, taken from the Ethereum bytecode instruction set. Every bytecode instruction consumes a certain amount of gas. Each client runs an instance of the Ethereum virtual machine (EVM), which executes calls to smart contracts and runs their sequence of instructions. While users may author smart contracts in higher level languages such as Solidity, these contracts must ultimately be compiled into EVM bytecode, since it is the bytecode that is published in the blockchain. The virtual machine specification, the bytecode instruction set, and all associated gas costs are described in Ethereum’s “Yellow Paper” .
3.2 Mining and Validation
Smart contracts are first executed by miners, or nodes that repeatedly propose new blocks to append to the blockchain. When a miner creates a block, it selects a sequence of client transactions and executes their smart contract codes in sequence, transforming the old contract state into a new state.
Once a block has been appended to the blockchain, that block’s smart contracts are re-executed by validators, or nodes that reconstruct (and check) the current blockchain state. Each miner validates blocks proposed by other miners, and older blocks are validated by newly-joined miners, or by clients querying the contract state. Once a contract is in a block, it is effectively re-executed forever (in Ethereum), so contract executions by validators vastly exceed executions by miners.
As noted earlier, one drawback of the Ethereum protocol is that a block’s contracts are executed in a one-at-a-time order, so miners and validators cannot exploit modern multicore architectures. Contracts cannot be executed concurrently in a naïve way, because they share storage, and may be subject to data conflicts, that is, concurrent accesses to the same the storage variables.
In the absence of explicit concurrency guidelines, we execute transactions speculatively in parallel, allowing non-conflicting contracts to commit, but rolling back conflicting transactions, and running them sequentially in a second phase.
A novel aspect of this study is that we analyze the effectiveness of speculation against the historical record of transactions actually executed on the Ethereum blockchain, replaying their bytecode instructions. We are not aware of an Ethereum virtual machine implementation that supports concurrency, so we simulate concurrent transaction execution by stepping through each transaction’s instructions, using eager conflict detection to sort each block’s transactions into a conflict-free parallel bin, and a conflicted sequential bin. This strategy is simple and scalable, a natural starting point for an empirical study.
4 The Baseline Experiment
Here we describe the baseline experiment testing the effectiveness of a simple speculative execution strategy against historical transaction data from the Ethereum blockchain. In Section 5, we describe variations on this baseline strategy that probe the sensitivity of our measurements, as well as alternative speculative strategies.
4.1 Setting up the Experiment
We set up an Ethereum node by installing and running the Ethereum Go client, also known as geth . The geth client allows one to synchronize with other network nodes and reconstruct the blockchain by validating each block. The client comes packaged with an interface for fetching data from the blockchain, as well as debugging tools for inspecting transactions. The debugging API includes a utility for reproducing the bytecode trace of a given transaction.
The geth client can run in various synchronization modes, which determine what and how much old state the client records. For example, a client running in light mode will keep track of the blockchain’s current state only, while in archive mode, the client maintains the full log of all previous states.
Using the geth API, it is straightforward to retrieve any given smart contract’s bytecode. However, the only way to tell how that bytecode affects the blockchain state is by re-executing each transaction before it. Therefore we use geth’s tracing utility to re-execute transactions, running the client in archive mode so it can reference past states. We found that reconstructing the blockchain from scratch in this way was much too slow to be feasible on an ordinary computer (an archive blockchain sync could not even keep up with the current blockchain growth rate), so we obtained a baseline copy of the blockchain from the ConSenSys archive  dating from Ethereum’s origins in 2016 to early October 2017. Starting from this base, we used the geth utilities to synchronize up to December 2017.
Between these dates, the Ethereum blockchain contains about 4 million blocks and 100 million transactions, far too much data to analyze in detail in a reasonable time with reasonable resources. Instead, we chose to focus on seven historical periods between July 2016 (after the DAO fork) and December 2017. Each period spans roughly one week, with consecutive periods separated by 11 weeks, so that each day of the month is considered. Due to computational limitations, we analyze every 10th block in each of the seven historical periods. Figure 2 shows these periods superimposed on the number of transactions per day at that time.
|1||Sept 1 – 8, 2016||4200||32502||11779|
|2||Nov 17 – 24, 2016||4200||32648||11070|
|3||Feb 2 – 9, 2017||3600||26415||10586|
|4||Apr 20 – 27, 2017||4100||56051||23684|
|5||July 6 – 13, 2017||3400||197003||84219|
|6||Sept 21 – 28, 2017||2100||200980||96043|
|7||Dec 7 – 14, 2017||4100||557471||255872|
4.2 The Greedy Concurrent EVM
We simulated a concurrent EVM that executes transactions speculatively in parallel using the following greedy strategy. For each block, execution proceeds in two phases, an initial concurrent phase, and a subsequent sequential phase. We consider execution engines with either 16, 32, or 64 threads. In the concurrent phase, each thread chooses a transaction from the block and executes it speculatively. If that transaction encounters a conflict, the transaction’s effects are rolled back, and that transaction is deferred to the second, sequential phase. When a thread finishes executing a transaction, it picks another to execute, continuing until all transactions in the current block have been chosen.
The second phase starts when the first phase is complete: the transactions that encountered data conflicts in the first phase are re-executed sequentially. In the second phase, data conflicts are not an issue since transactions are explicitly serialized and state changes committed sequentially.
Two transactions conflict if they access the same storage location, and at least one access is a write. Transactions that do not conflict are said to commute, because interleaving them in any order yields the same transaction and storage states. At a more granular level, two bytecode operations conflict if applying them in different orders yields different storage states. Most bytecode operations, such as arithmetic operations and others that interact with local state, commute with one another. Bytecode operations that interact with shared state, on the other hand, can potentially conflict.
The EVM operations SLOAD and SSTORE read from and write to persistent storage, respectively, and are used for nearly every state access and state modification. By far the most common conflicts arise from conflicting these two operations. Other kinds of conflicts, while possible, are assumed to be too rare to monitor. For example, one transaction might create a new contract, while another calls the newly created contract in a way that creates a race condition: the call may or may not arrive before the contract is initialized. There is also a bytecode operation that reads the a given account’s balance, which is part of the blockchain’s shared state.
We detect conflicts by associating a read-write lock with each storage location. Each SLOAD (respectively, SSTORE) operation requests that location’s lock in read (write) mode. If a transaction requests a lock that is already held in a conflicting mode by another transaction, the requesting transaction is rolled back and deferred to the next phase. No locks are released until the concurrent phase ends, even those held by aborted transactions. This is to ensure that no interleaving of transactions in concurrent bin result in a conflict when re-executed by validators.
4.3 Sampling and Evaluation
The Ethereum blockchain over the range of dates studied is much too large to analyze in detail in a reasonable amount of time, using reasonable computational resources. Instead, we chose to focus on seven historical periods between July 2016 (after the DAO fork) and December 2017. Each period spans roughly one week, with consecutive periods separated by 11 weeks, ensuring that each day of the month is evenly sampled. Due to computational limitations, we analyzed every 10th block in each of those seven historical periods.
Our principal figure of merit is speed-up: the ratio between the time to execute a block’s transactions sequentially, and the time to execute the same transactions concurrently and speculatively. For now, we use cumulative gas costs as a proxy for time, measuring the ratio between (1) the gas consumed to execute a block’s transactions sequentially, versus (2) the maximum gas used by any thread in the parallel phase plus the gas needed to execute the sequential phase. Note that a conflicted transaction may be counted twice, once in the concurrent phase, and once in the sequential phase.
4.4 Baseline Results
Figure 4 summarizes speed-up statistics over the seven historical periods, along with the conflict rates, or the percentage of contract calls that abort per block. The average speed-up and conflict rate are shown for simulated VMs of 16, 32, and 64 cores, where averages are weighted by the number of contract call transactions in each block.
Earlier periods display higher speed-ups and lower conflict rates because transaction volume and contention are low. For example, speed-up is as high as 3.23 in the second historical period on a simulated EVM with 16 cores, and this number rises to 8.87 for a 64 core EVM. During the same interval, contract calls abort at a rate of only 20%.
As the volume of transactions increases over time, however, so does the rate of transaction conflict, so more time is spent sequentially re-executing transactions aborted during the concurrent phase. This naturally leads to lower speed-ups, since there is less opportunity to parallelize transaction execution. Indeed, during the December 2017 period, with 16 threads, roughly 34% of transactions abort. Nevertheless, it is notable that there is still a modest but positive speed-up of 1.13 even then. Moreover, this speed-up effectively doubles, to slightly more than 2 when there are 64 threads. When transaction volume is higher, using more threads yields more speed-up.
4.5 Speed-up Distributions
The average speed-up of each historical period provides little insight into how the blocks’ speed-ups are distributed in each period. Here, we further analyze the performance of speculative execution by looking at the distributions of these speed-ups. Due to space limitations, we focus on historical periods 5, 6, and 7, since these have the highest transaction volumes.
Speed-up distributions of the three periods are depicted in Figure 5. Each plot is normalized to represent an approximate probability density, so that the y-axis is interpreted as a relative count. Since there is relatively less contention in periods 5 and 6, their speed-up distributions are flatter, with longer tails. This is because more blocks during these periods are able to exhibit a higher amount of speed-up. However, when the transaction rate is higher, as in seventh period of December 2017, the distributions are concentrated closed to 1, since more work is wasted as the number of aborted transactions rises.
As noted before, some blocks may realize a slowdown when using our concurrent execution strategy, a result of the cost of aborting and re-executing conflicting transactions. The relative number of such blocks is shown in plots of Figure 5, where regions to the left of represent blocks that are slowed down. Making more cores available to the EVM appears to reduce the number of slowed-down blocks for each of the three historical periods. Though fewer than 3% blocks are slowed down for both periods 5 and 6 when using 16 cores, this percentage jumps to 46% for Period 7. However, when the EVM’s number of cores is increased to 32, the percentage of blocks that are slowed down drops substantially to only 13%.
4.6 Storage Hot-Spots
Next, we investigate transaction contention by analyzing how often certain storage addresses are accessed by transactions. In particular, we look at memory addresses that are conflict points for pairs of transactions, and how many times each such address results in a conflict. Addresses that attract a high number of conflicts are informally called hot-spots.
In Figure 6, we plot histograms illustrating the number of conflicts per address, for historical periods 5, 6, and 7. Each storage address with at least one conflict is binned according to how many conflicts occurred at that address. Periods 5 and 6 have relatively few storage addresses with high contention, and this pattern is similar to the earlier historical periods. However, Period 7’s histogram has a much heavier tail than the other two histograms, meaning that there are many more storage addresses with larger numbers of conflicts. In other words, there is a handful of addresses that many different transactions attempt to access, most likely a result of so many transactions calling the same few contracts. We elaborate on this observations in Figure 5.6 below.
5 Alternative Experiments
We extended the baseline experiment with a number of other experiments intended to test the effectiveness of alternative strategies, and to test the sensitivity of our approximations.
|Sampling rate||Speed-up||Abort rate|
|16 cores||32 cores||64 cores||16 cores||32 cores||64 cores|
5.2 Multiple Phases
The greedy, two-phase strategy can be generalized to encompass multiple concurrent phases, so that each transaction that was deferred in one concurrent phase is instead re-executed in another subsequent concurrent phase. It is possible that multiple concurrent phases could provide additional speed-up. However, as illustrated in Figure 8, we found in our experiments that executing two concurrent phases almost always yields less speed-up than executing a single concurrent phase. This decrease is due to the duplicate work performed by transactions rolled back in multiple phases, with not enough additional speed-up yielded in the latter concurrent phases. Therefore, in practice, one current phase is sufficient to realize almost all potential concurrency.
5.3 Data Conflicts
In our simulated concurrent EVM, transactions access storage addresses by first acquiring a read-write lock. This allows multiple transactions to read an address, with no conflict, if there are no concurrent writers. In principle, this decreases the number of conflicts when compared to using mutex locks, hence increasing the potential speed-up.
To determine whether read-write locks reduce data conflicts in practice, we investigated the effect of simplifying the conflict model by merging all data accesses into a single conflict set, by using mutex locks for conflict detection. With the exception of the last historical period in which speed-up is already low, the speed-ups were substantially worse than the speed-ups obtained by distinguishing between read and write accesses. See Figure 9 for a comparison of the two locking schemes when using 16 cores. These results suggest that there is significant value in implementing a concurrent EVM with read-write locks, instead of the simpler mutex locks.
5.4 Proxies for Time
Since we do not have access to an actual concurrent EVM, we must estimate how long it takes to execute each transaction. There are two straightforward choices: we can count the number of instructions executed by each transaction, or we can tally the gas cost of executing the transaction’s instructions. The first choice assumes that each EVM instruction takes roughly the same time to execute, while the second assumes that instruction gas cost is roughly proportional to execution time. For example, the arithmetic operations MUL and DIV require 5 units of gas, while an SSTORE instruction can cost as much as 20000 units of gas.
All speed-ups reported so far were measured in terms of gas costs. As a sanity check, for 16 cores, we recomputed speed-ups using instruction count as a proxy for time. These speed-ups are shown in Figure 10. There does not appear to be any significant qualitative difference between gas cost and instruction count.
5.5 Static Conflict Prediction
If we were able to predict whether a transaction would abort if executed speculatively, then we could save the cost of transaction roll-back and retry. We can simulate the effect of a perfect conflict predictor simply by ignoring the cost of aborted transactions. However, doing so yield a negligible change in speed-ups, less than in most cases. The only exception is the very last period (December 2017), where contention was very high. For that historical period, the average speed-up increases from to . These numbers suggest that static conflict analysis, if it accurate enough, may be yield modest gains during periods of high contention.
5.6 Omitting Hot-spot Contracts
In most of the previous analysis, period 7 stands out among the selected historical periods as being particularly high volume and contentious. Recall that this period was sampled from December 2017, which is close to peak Ethereum transaction activity. More specifically, period 7 occurred when there was great interest from the general public over CryptoKitties  , a recreational game deployed on Ethereum in which users create, breed, and trade virtual cats. The popularity of CryptoKitties is especially apparent when using any blockchain explorer to browse Ethereum transactions during December 2017. The popularity of CryptoKitties was responsible for congesting the Ethereum network, though interest in it has since died down.
In light of the CryptoKitties frenzy, we reanalyzed period 7 under a hypothetical scenario in which the CryptoKitties contracts 111The vast majority of calls to CryptoKitties are to its core contract (which has address hash 0x06012c8cf97bead5deae237070f9587f8e7a266d), and to an auction contract (which has address hash 0xb1690c08e213a35ed9bab7b318de14420fb57d8c). did not exist. This is easily accomplished by ignoring all calls to the CryptoKitties contracts when tracing each block’s transactions and replaying them, and calculating the resulting statistics.
After filtering out all calls to CryptoKitties contracts, speed-up using 16 cores rises from 1.13 to 1.65. These calls accounted for about 31% of all contract calls, and furthermore, by filtering out CryptoKitties, the number of contract calls per block drops from 62 to 43, which is much closer to the 46 calls per block in period 6. While a speed-up of 1.65 does not quite match speed-up from older periods, it is still clear from this simple analysis that much of the contention in period 7 is caused solely by CryptoKitties.
To further illustrate the impact that CryptoKitties had, we reproduce the conflict histogram from the previous section, using the same hypothetical scenario with no CryptoKitties. Recall that the conflict histogram of period 7 had a much heavier tail than the corresponding histograms of older intervals. However when CryptoKitties is removed, period 7’s histogram (Figure 11(a)) has a much thinner tail, resembling the other histograms. Indeed, in period 6, 10% of conflicting storage addresses have at least 5 conflicts, but in period 7, this same number is 30%. However, if CryptoKitties contracts are ignored, then only 14% or conflicting address have at least 5 conflicts. These numbers demonstrate that CryptoKitties is responsible for many storage hot-spots.
We generalize this analysis to the other historical periods by determining the top five most conflicting smart contracts in each period, and reproducing scenarios where none of the highly conflicting contracts were called. As shown in Figure 11(b), this results in a noticeable speed-up in each period, not just the 7th. Most of these highly conflicting contracts are actually token contracts; in fact, the most contentious contract from each period is either a token or a token exchange contract. Therefore, analyzing these small sets of contracts may provide insight into how to reduce contention when speculatively executing smart contracts in parallel.
As noted, this study is exploratory, incorporating various approximations and omissions. Most such omissions are the result of the absence of a standard concurrent EVM implementation. This study does not account for some EVM overheads, such as the costs for value transfers, where one account transfers ether directly to another, without modifying storage. Value transfers typically commute, and are likely much faster to execute than contract calls, though they do make up the majority of all Ethereum transactions. Other sources of overhead include solving a cryptographic puzzle to compute proof of work, which would affect miners’ speed-up but not the validators’.
In the absence of a timing model for a concurrent EVM, this study uses gas costs (or instruction counts) as proxies for time when computing speed-up. As noted, both proxies yield essentially equivalent results.
This study relies on sampled blocks because the volume of data in the Ethereum blockchain is simply too large to make exhaustive analysis practical or rewarding. An archive synchronization of the blockchain was a major computational overhead for this study, in addition to recovering transaction traces. These overheads may be reduced as further Ethereum tools and utilities are developed.
There are several reasons why speculative parallelism may sometimes yield little, or even negative speed-up. For example, a block might contain one transaction substantially longer than the others, whose execution time dominates the block execution time. In this case, it is impossible to achieve much speed-up, no matter how these transactions are scheduled and distributed among multiple cores. Or if a block contains very few contract call transactions, there is little opportunity for speed-up. If a block’s transactions all access the same storage location, perhaps because they all access the same popular contract , then speculation will produce a negative speed-up as a result of the cost of rolling back so many misspeculated transactions.
Our results suggest that a simple speculative strategy based on read-write set overlap can produce non-trivial speed-ups, but that such speed-ups will decline as transaction rates and conflict rates increase. More aggressive strategies, such as adding additional parallel phases, seem to provide little additional benefit, because conflict appears to be bursty: if one transaction conflicts with another, then it probably conflicts with multiple others.
The results of this study suggest that the most promising way to further increase parallelism in Ethereum-style smart contract execution is to reduce the conflict rate, perhaps by focusing on reducing unnecessary conflicts. We observed that splitting transactions’ data sets into read sets and write sets decreased conflict rates substantially, suggesting that conflict rates are sensitive to the semantics of concurrent operations on shared data. This observation suggests that conflict rates might be reduced even further if the execution engine could do a better job of recognizing when operations commute at the semantic level. For example, transactions that increment or decrement the same account balance (a common occurrence) have overlapping read and write sets, and are therefore deemed to conflict. At the semantic level, however, these operations commute (in the absence of overflow or underflow), so as long as the virtual machine’s memory operations are atomic, those operations need not conflict. (Our study could not detect which conflicts are real, and which are artifacts, because only compiled bytecode was available for analysis).
It might be profitable to investigate the effects of endowing the virtual machine with intrinsic data types such as atomic counters or atomic sets that provide many commuting mutator operations. Studying highly conflicting token contracts may provide insight into which kinds of data types or operations would best alleviate contention.
Periods of high contention and low speed-up are caused by a relatively small number of popular contracts. Currently, smart contract designers have no guidance how to avoid speculative data conflicts, nor any incentive to do so. Our results suggest that there is a need to devise incentives for smart contract programmers to design contracts in ways that reduce conflicts, either by eliminating spurious conflicts, or by exploiting improved commuting bytecode instructions.
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