Solana: A new architecture for a highperformance blockchain v0.8.13
Anatoly Yakovenko
anatoly@solana.io
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Abstract
This paper proposes a new blockchain architecture based on Proofof History (PoH) - a proof for verifying order and passage of time
between events. PoH is used to encode trustless passage of time into
a ledger - an append only data structure. When used alongside aconsensus algorithm such as Proof of Work (PoW) or Proof of Stake(PoS), PoH can reduce messaging overhead in a Byzantine Fault Tolerant replicated state machine, resulting inn sub-second finality times.
This paper also proposes two algorithms that leverage the time keeping properties of the PoH ledger - a PoS algorithm that can recoverfrom partitions of any size and an efficient streaming Proof of Replication (PoRep). The combination of PoRep and PoH provides a defense
against forgery of the ledger with respect to time (ordering) and storage. The protocol is analyzed on a 1 gbps network, and this paper
shows that throughput up to 710k transactions per second is possible
with todays hardware.

11Introduction

Blockchain is an implementation of a fault tolerant replicated state machine.
Current publicly available blockchains do not rely on time, or make a weak
assumption about the participants abilities to keep time [4, 5]. Each node in
the network usually relies on their own local clock without knowledge of any
other participants clocks in the network. The lack of a trusted source of timemeans that when a message timestamp is used to accept or reject a message,
there is no guarantee that every other participant in the network will makethe exact same choice. The PoH presented here is designed to create a ledger
with verifiable passage of time, i.e. duration between events and message
ordering. It is anticipated that every node in the network will be able to relyon the recorded passage of time in the ledger without trust.

2

Outline

The remainder of this article is organized as follows. Overall system design isdescribed in Section 3. In depth description of Proof of History is describedin Section 4. In depth description of the proposed Proof of Stake consensus
algorithm is described in Section 5. In depth description of the proposed
fast Proof of Replication is described in Section 6. System Architecture andperformance limits are analyzed in Section 7. A high performance GPU
friendly smart contracts engine is described in Section 7.5

3

Network Design

As shown in Figure 1, at any given time a system node is designated as
Leader to generate a Proof of History sequence, providing the network global
read consistency and a verifiable passage of time. The Leader sequences user
messages and orders them such that they can be efficiently processed by othernodes in the system, maximizing throughput. It executes the transactions
on the current state that is stored in RAM and publishes the transactions
and a signature of the final state to the replications nodes called Verifiers.
Verifiers execute the same transactions on their copies of the state, and publish their computed signatures of the state as confirmations. The publishedconfirmations serve as votes for the consensus algorithm.

2

Figure 1: Transaction flow throughout the network.

In a non-partitioned state, at any given time, there is one Leader in the
network. Each Verifier node has the same hardware capabilities as a Leader
and can be elected as a Leader, this is done through PoS based elections.
Elections for the proposed PoS algorithm are covered in depth in Section 5.6.
In terms of CAP theorem, Consistency is almost always picked over Availability in an event of a Partition. In case of a large partition, this paper
proposes a mechanism to recover control of the network from a partition of
any size. This is covered in depth in Section 5.12.

4

Proof of History

Proof of History is a sequence of computation that can provide a way tocryptographically verify passage of time between two events. It uses a cryptographically secure function written so that output cannot be predictedfrom the input, and must be completely executed to generate the output.
The function is run in a sequence on a single core, its previous output as the3current input, periodically recording the current output, and how many times
its been called. The output can then be re-computed and verified by external
computers in parallel by checking each sequence segment on a separate core.
Data can be timestamped into this sequence by appending the data (or ahash of some data) into the state of the function. The recording of the state,index and data as it was appended into the sequences provides a timestampthat can guarantee that the data was created sometime before the next hash
was generated in the sequence. This design also supports horizontal scaling
as multiple generators can synchronize amongst each other by mixing theirstate into each others sequences. Horizontal scaling is discussed in depth in
Section 4.4

4.1

Description

The system is designed to work as follows. With a cryptographic hash function, whose output cannot be predicted without running the function (e.g.
sha256, ripemd, etc.), run the function from some random starting value
and take its output and pass it as the input into the same function again.
Record the number of times the function has been called and the output at
each call. The starting random value chosen could be any string, like theheadline of the New York times for the day.
For example:
PoH Sequence
Index
1
2
3

Operation
Output Hash
sha256(”any random starting value”)
hash1
sha256(hash1)
hash2
sha256(hash2)
hash3

Where hashN represents the actual hash output.
It is only necessary to publish a subset of the hashes and indices at aninterval.
For example:

4

PoH Sequence
Index Operation
Output Hash1
sha256(”any random starting value”)
hash1
200
sha256(hash199)
hash200300
sha256(hash299)
hash300
As long as the hash function chosen is collision resistant, this set of hashes
can only be computed in sequence by a single computer thread. This follows
from the fact that there is no way to predict what the hash value at index
300 is going to be without actually running the algorithm from the starting
value 300 times. Thus we can thus infer from the data structure that realtime has passed between index 0 and index 300.
In the example in Figure 2, hash 62f51643c1 was produced on count510144806912 and hash c43d862d88 was produced on count 510146904064.
Following the previously discussed properties of the PoH algorithm, we cantrust that real time passed between count 510144806912 and count 510146904064.

Figure 2: Proof of History sequence

5

4.2

Timestamp for Events

This sequence of hashes can also be used to record that some piece of data
was created before a particular hash index was generated. Using a ‘combine‘function to combine the piece of data with the current hash at the current
index. The data can simply be a cryptographically unique hash of arbitrary
event data. The combine function can be a simple append of data, or any
operation that is collision resistant. The next generated hash represents a
timestamp of the data, because it could have only been generated after thatspecific piece of data was inserted.
For example:

PoH Sequence
Index Operation
Output Hash1
sha256(”any random starting value”)
hash1
200
sha256(hash199)
hash200300
sha256(hash299)
hash300
Some external event occurs, like a photograph was taken, or any arbitrary
digital data was created:

PoH Sequence With Data
Index Operation
Output Hash1
sha256(”any random starting value”)
hash1
200
sha256(hash199)
hash200300
sha256(hash299)
hash300
336
sha256(append(hash335, photograph sha256))
hash336
Hash336 is computed from the appended binary data of hash335 and thesha256 of the photograph. The index and the sha256 of the photograph arerecorded as part of the sequence output. So anyone verifying this sequence
can then recreate this change to the sequence. The verifying can still be donein parallel and its discussed in Section 4.3
Because the initial process is still sequential, we can then tell that thingsentered into the sequence must have occurred sometime before the futurehashed value was computed.
6

POH Sequence
Index
1
200
300
336
400
500
600
700

Operation
Output Hash
sha256(”any random starting value”)
hash1
sha256(hash199)
hash200
sha256(hash299)
hash300
hash336
sha256(append(hash335, photograph1 sha256))
sha256(hash399)
hash400
sha256(hash499)
hash500
hash600
sha256(append(hash599, photograph2 sha256))
sha256(hash699)
hash700
Table 1: PoH Sequence With 2 Events

In the sequence represented by Table 1, photograph2 was created before
hash600, and photograph1 was created before hash336. Inserting the data
into the sequence of hashes results in a change to all subsequent values in thesequence. As long as the hash function used is collision resistant, and thedata was appended, it should be computationally impossible to pre-computeany future sequences based on prior knowledge of what data will be integrated into the sequence.
The data that is mixed into the sequence can be the raw data itself, orjust a hash of the data with accompanying metadata.
In the example in Figure 3, input cfd40df8... was inserted into the
Proof of History sequence. The count at which it was inserted is 510145855488
and the state at which it was inserted it is 3d039eef3. All the future generated hashes are modified by this change to the sequence, this change isindicated by the color change in the figure.
Every node observing this sequence can determine the order at which all
events have been inserted and estimate the real time between the insertions.

7

Figure 3: Inserting data into Proof of History

4.3

Verification

The sequence can be verified correct by a multicore computer in significantly
less time than it took to generate it.
For example:
Core 1
Index Data
Output Hash200
sha256(hash199)
hash200300
sha256(hash299)
hash300
Core 2
Index Data
Output Hash300
sha256(hash299)
hash300400
sha256(hash399)
hash400
Given some number of cores, like a modern GPU with 4000 cores, theverifier can split up the sequence of hashes and their indexes into 4000 slices,
and in parallel make sure that each slice is correct from the starting hash to
the last hash in the slice. If the expected time to produce the sequence isgoing to be:8Figure 4: Verification using multiple cores

Total number of hashes
Hashes per second for 1 core
The expected time to verify that the sequence is correct is going to be:
Total number of hashes(Hashes per second per core * Number of cores available to verify)
In the example in Figure 4, each core is able to verify each slice of thesequence in parallel. Since all input strings are recorded into the output, withthe counter and state that they are appended to, the verifiers can replicate
each slice in parallel. The red colored hashes indicate that the sequence wasmodified by a data insertion.

4.4

Horizontal Scaling

Its possible to synchronize multiple Proof of History generators by mixing thesequence state from each generator to each other generator, and thus achieve
horizontal scaling of the Proof of History generator. This scaling is done
without sharding. The output of both generators is necessary to reconstruct
the full order of events in the system.

9

PoH Generator A
Index HashData1
hash1a2
hash2a hash1b3
hash3a4
hash4aPoH Generator B
Index HashData1
hash1b2
hash2b hash1a3
hash3b4
hash4bGiven generators A and B, A receives a data packet from B (hash1b),which contains the last state from Generator B, and the last state generator
B observed from Generator A. The next state hash in Generator A then depends on the state from Generator B, so we can derive that hash1b happened
sometime before hash3a. This property can be transitive, so if three generators are synchronized through a single common generator A ↔ B ↔ C,
we can trace the dependency between A and C even though they were notsynchronized directly.
By periodically synchronizing the generators, each generator can thenhandle a portion of external traffic, thus the overall system can handle alarger amount of events to track at the cost of true time accuracy due to
network latencies between the generators. A global order can still be achievedby picking some deterministic function to order any events that are within
the synchronization window, such as by the value of the hash itself.
In Figure 5, the two generators insert each others output state and record
the operation. The color change indicates that data from the peer had modified the sequence. The generated hashes that are mixed into each stream
are highlighted in bold.
The synchronization is transitive. A ↔ B ↔ C There is a provable orderof events between A and C through B.
Scaling in this way comes at the cost of availability. 10 × 1 gbps connections with availability of 0.999 would have 0.99910 = 0.99 availability.

4.5

Consistency

Users are expected to be able to enforce consistency of the generated sequence and make it resistant to attacks by inserting the last observed outputof the sequence they consider valid into their input.

10Figure 5: Two generators synchronizing

Index
10
20
30
40

PoH Sequence A
Data Output Hashhash10a
Event1
hash20a
Event2
hash30a
Event3hash40a

PoH Hidden Sequence B
Index Data Output Hash10hash10b20
Event3hash20b30
Event2
hash30b40
Event1hash40b

A malicious PoH generator could produce a second hidden sequence withthe events in reverse order, if it has access to all the events at once, or is able
to generate a faster hidden sequence.
To prevent this attack, each client-generated Event should contain within
itself the latest hash that the client observed from what it considers to bea valid sequence. So when a client creates the ”Event1” data, they should
append the last hash they have observed.

11

PoH Sequence A
Data

Index
10
20
Event1 = append(event1 data, hash10a)30
Event2 = append(event2 data, hash20a)
40
Event3 = append(event3 data, hash30a)

Output Hashhash10ahash20ahash30ahash40a

When the sequence is published, Event3 would be referencing hash30a,
and if its not in the sequence prior to this Event, the consumers of thesequence know that its an invalid sequence. The partial reordering attack
would then be limited to the number of hashes produced while the client has
observed an event and when the event was entered. Clients should then beable to write software that does not assume the order is correct for the shortperiod of hashes between the last observed and inserted hash.
To prevent a malicious PoH generator from rewriting the client Eventhashes, the clients can submit a signature of the event data and the last
observed hash instead of just the data.
PoH Sequence A
Index
10
20
30
40

Data

Output Hashhash10a

Event1 = sign(append(event1 data, hash10a),Client Private Key)
Event2 = sign(append(event2 data, hash20a),
Client Private Key)
Event3 = sign(append(event3 data, hash30a),
Client Private Key)hash20ahash30ahash40a

Verification of this data requires a signature verification, and a lookup of
the hash in the sequence of hashes prior to this one.
Verify:(Signature, PublicKey, hash30a, event3 data) = Event3
Verify(Signature, PublicKey, Event3)
Lookup(hash30a, PoHSequence)
In Figure 6, the user-supplied input is dependent on hash 0xdeadbeef...
existing in the generated sequence sometime before its inserted. The blue
12

Figure 6: Input with a back reference.

top left arrow indicates that the client is referencing a previously produced
hash. The clients message is only valid in a sequence that contains the hash0xdeadbeef.... The red color in the sequence indicates that the sequence
has been modified by the clients data.

4.6

Overhead4000 hashes per second would generate an additional 160 kilobytes of data,
and would require access to a GPU with 4000 cores and roughly 0.25-0.75
milliseconds of time to verify.

4.7

Attacks

4.7.1

Reversal

Generating a reverse order would require an attacker to start the malicious
sequence after the second event. This delay should allow any non malicious
peer to peer nodes to communicate about the original order.

13

4.7.2

Speed

Having multiple generators may make deployment more resistant to attacks.
One generator could be high bandwidth, and receive many events to mix
into its sequence, another generator could be high speed low bandwidth that
periodically mixes with the high bandwidth generator.
The high speed sequence would create a secondary sequence of data thatan attacker would have to reverse.
4.7.3

Long Range Attacks

Long range attacks involve acquiring old discarded client Private Keys, andgenerating a falsified ledger [10]. Proof of History provides some protection
against long range attacks. A malicious user that gains access to old private
keys would have to recreate a historical record that takes as much time as theoriginal one they are trying to forge. This would require access to a fasterprocessor than the network is currently using, otherwise the attacker would
never catch up in history length.
Additionally, a single source of time allows for construction of a simpler
Proof of Replication (more on that in Section 6). Since the network is designed so that all participants in the network will rely on a single historical
record of events.
PoRep and PoH together should provide a defense of both space and timeagainst a forged ledger.

5

Proof of Stake Consensus

5.1

Description

This specific instance of Proof of Stake is designed for quick confirmation of
the current sequence produced by the Proof of History generator, for voting
and selecting the next Proof of History generator, and for punishing any misbehaving validators. This algorithm depends on messages eventually arriving
to all participating nodes within a certain timeout.

14

5.2

Terminology

bonds Bonds are equivalent to a capital expense in Proof of Work. A minerbuys hardware and electricity, and commits it to a single branch in a
Proof of Work blockchain. A bond is coin that the validator commits
as collateral while they are validating transactions.
slashing The proposed solution to the nothing at stake problem in Proofof Stake systems [7]. When a proof of voting for a different branch
is published, that branch can destroy the validators bond. This is an
economic incentive designed to discourage validators from confirming
multiple branches.
super majority A super majority is 23 rds of the validators weighted by theirbonds. A super majority vote indicates that the network has reachedconsensus, and at least 13 rd of the network would have had to votemaliciously for this branch to be invalid. This would put the economic
cost of an attack at 31 rd of the market cap of the coin.

5.3

Bonding

A bonding transaction takes a amount of coin and moves it to a bonding
account under the users identity. Coins in the bonding account cannot be
spent and have to remain in the account until the user removes them. The
user can only remove stale coins that have timed out. Bonds are valid after
super majority of the current stakeholders have confirmed the sequence.

5.4

Voting

It is anticipated that the Proof of History generator will be able to publish
a signature of the state at a predefined period. Each bonded identity must
confirm that signature by publishing their own signed signature of the state.
The vote is a simple yes vote, without a no.
If super majority of the bonded identities have voted within a timeout,
then this branch would be accepted as valid.

15

5.5

Unbonding

Missing N number of votes marks the coins as stale and no longer eligible forvoting. The user can issue an unbonding transaction to remove them.
N is a dynamic value based on the ratio of stale to active votes. N
increases as the number of stale votes increases. In an event of a large
network partition, this allows the larger branch to recover faster then thesmaller branch.

5.6

ElectionsElection for a new PoH generator occur when the PoH generator failure is
detected. The validator with the largest voting power, or highest public key
address if there is a tie is picked as the new PoH generator.
A super majority of confirmations are required on the new sequence. If
the new leader fails before a super majority confirmations are available, thenext highest validator is selected, and a new set of confirmations is required.
To switch votes, a validator needs to vote at a higher PoH sequence
counter, and the new vote needs to contain the votes it wants to switch.
Otherwise the second vote will be slashable. Vote switching is expected to
be designed so that it can only occur at a height that does not have a supermajority.
Once a PoH generator is established, a Secondary can be elected to takeover the transactional processing duties. If a Secondary exists, it will beconsidered as the next leader during a Primary failure.
The platform is designed so that the Secondary becomes Primary andlower rank generators are promoted if an exception is detected or on a predefined schedule.

5.7

Election Triggers

5.7.1

Forked Proof of History generator

PoH generators are designed with an identity that signs the generated sequence. A fork can only occur in case the PoH generators identity has been
compromised. A fork is detected because two different historical records havebeen published on the same PoH identity.

16

5.7.2

Runtime Exceptions

A hardware failure or a bug, or a intentional error in the PoH generator could
cause it to generate an invalid state and publish a signature of the state that
does not match the local validators result. Validators will publish the correct
signature via gossip and this event would trigger a new round of elections.
Any validators who accept an invalid state will have their bonds slashed.
5.7.3

Network Timeouts

A network timeout would trigger an election.

5.8

Slashing

Slashing occurs when a validator votes two separate sequences. A proof ofmalicious vote will remove the bonded coins from circulation and add themto the mining pool.
A vote that includes a previous vote on a contending sequence is not
eligible as proof of malicious voting. Instead of slashing the bonds, this voteremoves remove the currently cast vote on the contending sequence.
Slashing also occurs if a vote is cast for an invalid hash generated by the
PoH generator. The generator is expected to randomly generate an invalid
state, which would trigger a fallback to Secondary.

5.9

Secondary Elections

Secondary and lower ranked Proof of History generators can be proposed
and approved. A proposal is cast on the primary generators sequence. The
proposal contains a timeout, if the motion is approved by a super majorityof the vote before the timeout, the Secondary is considered elected, and willtake over duties as scheduled. Primary can do a soft handover to Secondary
by inserting a message into the generated sequence indicating that a handover
will occur, or inserting an invalid state and forcing the network to fallback
to Secondary.
If a Secondary is elected, and the primary fails, the Secondary will beconsidered as the first fallback during an election.

17

5.10

Availability

CAP systems that deal with partitions have to pick Consistency or Availability. Our approach eventually picks Availability, but because we havean objective measure of time, Consistency is picked with reasonable human
timeouts.
Proof of Stake verifiers lock up some amount of coin in a stake, whichallows them to vote for a particular set of transactions. Locking up coin is atransaction that is entered into a PoH stream, just like any other transaction.
To vote, a PoS verifier has to sign the hash of the state, as it was computed
after processing all the transactions to a specific position in the PoH ledger.
This vote is also entered as a transaction into the PoH stream. Looking at
the PoH ledger, we can then infer how much time passed between each vote,and if a partition occurs, for how long each verifier has been unavailable.
To deal with partitions with reasonable human timeframes, we proposea dynamic approach to unstake unavailable verifiers. When the number ofverifiers is high and above 23 , the unstaking process can be fast. The numberof hashes that must be generated into the ledger is low before the unavailable
verifiers stake is fully unstaked and they are no longer counted for consensus.
When the number of verifiers is below 32 rds but above 12 , the unstaking timer isslower, requiring a larger number of hashes to be generated before the missing
verifiers are unstaked. In a large partition, like a partition that is missing 21
or more of the verifiers, the unstaking process is very very slow. Transactions
can still be entered into the stream, and verifiers can still vote, but full 23 rdsconsensus will not be achieved until a very large amount of hashes have been
generated and the unavailable verifiers have been unstaked. The differencein time for a network to regain liveness allows us as customers of the network
human timeframes to pick a partition that we want to continue using.

5.11

RecoveryIn the system we propose, the ledger can be fully recovered from any failure.
That means, anyone in the world can pick any random spot in the ledger andcreate a valid fork by appending newly generated hashes and transactions.
If all the verifiers are missing from this fork, it would take a very very longtime for any additional bonds to become valid and for this branch to achieve
2
rds super majority consensus. So full recovery with zero available validators
3
would require a very large amount of hashes to be appended to the ledger,18

and only after all the unavailable validators have been unstaked will any new
bonds be able to validate the ledger.

5.12

Finality

PoH allows verifiers of the network to observe what happened in the past withsome degree of certainty of the time of those events. As the PoH generator is
producing a stream of messages, all the verifiers are required to submit theirsignatures of the state within 500ms. This number can be reduced further
depending on network conditions. Since each verification is entered into thestream, everyone in the network can validate that every verifier submitted
their votes within the required timeout without actually observing the voting
directly.

5.13

Attacks

5.13.1

Tragedy of Commons

The PoS verifiers simply confirm the state hash generated by the PoH generator. There is an economic incentive for them to do no work and simply
approve every generated state hash. To avoid this condition, the PoH generator should inject an invalid hash at a random interval. Any voters for thishash should be slashed. When the hash is generated, the network should
immediately promote the Secondary elected PoH generator.
Each verifier is required to respond within a small timeout - 500ms forexample. The timeout should be set low enough that a malicious verifier has
a low probability of observing another verifiers vote and getting their votes
into the stream fast enough.
5.13.2Collusion with the PoH generator

A verifier that is colluding with the PoH generator would know in advancewhen the invalid hash is going to be produced and not vote for it. This
scenario is really no different than the PoH identity having a larger verifier
stake. The PoH generator still has to do all the work to produce the state
hash.

19

5.13.3

Censorship

Censorship or denial of service could occur when a 13 rd of the bond holders
refuse to validate any sequences with new bonds. The protocol can defend
against this form of attack by dynamically adjusting how fast bonds become
stale. In the event of a denial of service, the larger partition will be designed
to fork and censor the Byzantine bond holders. The larger network will recover as the Byzantine bonds become stale with time. The smaller Byzantine
partition would not be able to move forward for a longer period of time.
The algorithm would work as follows. A majority of the network would
elect a new Leader. The Leader would then censor the Byzantine bondholders from participating. Proof of History generator would have to continue
generating a sequence, to prove the passage of time, until enough Byzantine
bonds have become stale so the bigger network has a super majority. The rate
at which bonds become stale would be dynamically based on what percentage
of bonds are active. So the Byzantine minority fork of the network would haveto wait much longer than the majority fork to recover a super majority. Oncea super majority has been established, slashing could be used to permanently
punish the Byzantine bond holders.
5.13.4

Long Range Attacks

PoH provides a natural defense against long range attacks. Recovering theledger from any point in the past would require the attacker to overtake thevalid ledger in time by outpacing the speed of the PoH generator.
The consensus protocol provides a second layer of defense, as any attack
would have to take longer then the time it takes to unstake all the valid
validators. It also creates an availability gap in the history of the ledger.
When comparing two ledgers of the same height, the one with the smallest
maximum partition can be objectively considered as valid.
5.13.5ASIC Attacks

Two opportunities for an ASIC attacks exist in this protocol - during partition, and cheating timeouts in Finality.
For ASIC attacks during Partitions, the Rate at which bonds are unstaked
is non-linear, and for networks with large partitions the rate is orders ofmagnitude slower then expected gains from an ASIC attack.

20

For ASIC attacks during Finality, the vulnerability allows for byzantine
validators who have a bonded stake wait for confirmations from other nodes
and inject their votes with a collaborating PoH generator. The PoH generator can then use its faster ASIC to generate 500ms worth of hashes in lesstime, and allow for network communication between PoH generator and thecollaborating nodes. But, if the PoH generator is also byzantine, there is no
reason why the byzantine generator wouldnt have communicated the exact
counter when they expect to insert the failure. This scenario is no different
than a PoH generator and all the collaborators sharing the same identity andhaving a single combined stake and only using 1 set of hardware.

6

Streaming Proof of Replication

6.1

Description

Filecoin proposed a version of Proof of Replication [6]. The goal of thisversion is to have fast and streaming verifications of Proof of Replication,
which are enabled by keeping track of time in Proof of History generated
sequence. Replication is not used as a consensus algorithm, but is a useful
tool to account for the cost of storing the blockchain history or state at ahigh availability.

6.2

Algorithm

As shown in Figure 7 CBC encryption encrypts each block of data in sequence, using the previously encrypted block to XOR the input data.
Each replication identity generates a key by signing a hash that has been
generated Proof of History sequence. This ties the key to a replicators identity, and to a specific Proof of History sequence. Only specific hashes can beselected. (See Section 6.5 on Hash Selection)
The data set is fully encrypted block by block. Then to generate a proof,
the key is used to seed a pseudorandom number generator that selects a
random 32 bytes from each block.
A merkle hash is computed with the selected PoH hash prepended to theeach slice.
The root is published, along with the key, and the selected hash thatwas generated. The replication node is required to publish another proof
21

Figure 7: Sequential CBC encryption

Figure 8: Fast Proof of Replication22

in N hashes as they are generated by Proof of History generator, where N
is approximately 12 the time it takes to encrypt the data. The Proof ofHistory generator will publish specific hashes for Proof of Replication at apredefined periods. The replicator node must select the next published hashfor generating the proof. Again, the hash is signed, and random slices are
selected from the blocks to create the merkle root.
After a period of N proofs, the data is re-encrypted with a new CBC key.

6.3

Verification

With N cores, each core can stream encryption for each identity. Total spacerequired is 2blocks ∗ N cores, since the previous encrypted block is necessary
to generate the next one. Each core can then be used to generate all the
proofs that derived from the current encrypted block.
Total time to verify proofs is expected to be equal to the time it takes toencrypt. The proofs themselves consume few random bytes from the block,so the amount of data to hash is significantly lower then the encrypted block
size. The number of replication identities that can be verified at the sametime is equal to the number of available cores. Modern GPUs have 3500+
cores available to them, albeit at 12 - 13 the clock speed of a CPU.

6.4

Key Rotation

Without key rotation the same encrypted replication can generate cheap
proofs for multiple Proof of History sequences. Keys are rotated periodicallyand each replication is re-encrypted with a new key that is tied to a unique
Proof of History sequence.
Rotation needs to be slow enough that its practical to verify replication
proofs on GPU hardware, which is slower per core than CPUs.

6.5

Hash Selection

Proof of History generator publishes a hash to be used by the entire networkfor encrypting Proofs of Replication, and for using as the pseudorandom
number generator for byte selection in fast proofs.
Hash is published at a periodic counter that is roughly equal to 21 the time
it takes to encrypt the data set. Each replication identity must use the same23

hash, and use the signed result of the hash as the seed for byte selection, orthe encryption key.
The period that each replicator must provide a proof must be smaller thanthe encryption time. Otherwise the replicator can stream the encryption anddelete it for each proof.
A malicious generator could inject data into the sequence prior to thishash to generate a specific hash. This attack is discussed more in 5.13.2.

6.6

Proof Validation

The Proof of History node is not expected to validate the submitted Proof
of Replication proofs. It is expected to keep track of number of pending andverified proofs submitted by the replicators identity. A proof is expected tobe verified when the replicator is able to sign the proof by a super majorityof the validators in the network.
The verifications are collected by the replicator via p2p gossip network,and submitted as one packet that contains a super majority of the validatorsin the network. This packet verifies all the proofs prior to a specific hash generated by the Proof of History sequence, and can contain multiple replicator
identities at once.

6.7

Attacks

6.7.1

Spam

A malicious user could create many replicator identities and spam the network with bad proofs. To facilitate faster verification, nodes are required
to provide the encrypted data and the entire merkle tree to the rest of the
network when they request verification.
The Proof of Replication that is designed in this paper allows for cheap
verification of any additional proofs, as they take no additional space. But
each identity would consume 1 core of encryption time. The replication target
should be set to a maximum size of readily available cores. Modern GPUs
ship with 3500+ cores.
6.7.2

Partial Erasure

A replicator node could attempt to partially erase some of the data to avoid
storing the entire state. The number of proofs and the randomness of the24seed should make this attack difficult.
For example, a user storing 1 terabyte of data erases a single byte fromeach 1 megabyte block. A single proof that samples 1 byte out of every
megabyte would have a likelihood of collision with any erased byte 1 − (1 −1/1, 000, 0000)1,000,000 = 0.63. After 5 proofs the likelihood is 0.99.
6.7.3

Collusion with PoH generator

The signed hash is expected to be used to seed the sample. If a replicator
could select a specific hash in advance then the replicator could erase allbytes that are not going to be sampled.
A replicator identity that is colluding with the Proof of History generator
could inject a specific transaction at the end of the sequence before the predefined hash for random byte selection is generated. With enough cores, anattacker could generate a hash that is preferable to the replicators identity.
This attack could only benefit a single replicator identity. Since all theidentities have to use the same exact hash that is cryptographically signedwith ECDSA (or equivalent), the resulting signature is unique for each replicator identity, and collision resistant. A single replicator identity would only
have marginal gains.
6.7.4

Denial of Service

The cost of adding an additional replicator identity is expected to be equal
to the cost of storage. The cost of adding extra computational capacity toverify all the replicator identities is expected to be equal to the cost of a CPU
or GPU core per replication identity.
This creates an opportunity for a denial of service attack on the networkby creating a large number of valid replicator identities.
To limit this attack, the consensus protocol chosen for the network can
select a replication target, and award the replication proofs that meet the desired characteristics, like availability on the network, bandwidth, geolocation
etc...
6.7.5

Tragedy of Commons

The PoS verifiers could simply confirm PoRep without doing any work. The
economic incentives should be lined up with the PoS verifiers to do work,25

Figure 9: System Architecture

like by splitting the mining payout between the PoS verifiers and the PoRep
replication nodes.
To further avoid this scenario, the PoRep verifiers can submit false proofsa small percentage of the time. They can prove the proof is false by providing
the function that generated the false data. Any PoS verifier that confirmed
a false proof would be slashed.

7

System Architecture

7.1

Components

7.1.1

Leader, Proof of History generator

The Leader is an elected Proof of History generator. It consumes arbitrary
user transactions and outputs a Proof of History sequence of all the transactions that guarantees a unique global order in the system. After each batchof transactions the Leader outputs a signature of the state that is the resultof running the transactions in that order. This signature is signed with theidentity of the Leader.

26

7.1.2State

A naive hash table indexed by the users address. Each cell contains the fullusers address and the memory required for this computation. For example
Transaction table contains:
0

31

63

95

127

159

Ripemd of Users Public Key

191

Account

223

255

unusedFor a total of 32 bytes.
Proof of Stake bonds table contains:
0

31

63

95

127

159

Ripemd of Users Public Key

191

223

255

Bond

Last VoteunusedFor a total of 64 bytes.
7.1.3

Verifier, State Replication

The Verifier nodes replicate the blockchain state and provide high availabilityof the blockchain state. The replication target is selected by the consensus
algorithm, and the validators in the consensus algorithm select and vote theProof of Replication nodes they approve of based on off-chain defined criteria.
The network could be configured with a minimum Proof of Stake bondsize, and a requirement for a single replicator identity per bond.
7.1.4

Validators

These nodes are consuming bandwidth from Verifiers. They are virtual nodes,
and can run on the same machines as the Verifiers or the Leader, or on
separate machines that are specific to the consensus algorithm configured forthis network.

7.2

Network Limits

Leader is expected to be able to take incoming user packets, orders themthe most efficient way possible, and sequences them into a Proof of History
sequence that is published to downstream Verifiers. Efficiency is based on

27

Figure 10: Generator network limits

memory access patterns of the transactions, so the transactions are ordered
to minimize faults and to maximize prefetching.

Incoming packet format:
0

31

63

95

127

159

191

Counter

Last Valid Hash

223

255

us






Fee
From






Signature 1 of 2
Signature 2 of 2Size 20 + 8 + 16 + 8 + 32 + 3232 = 148 bytes.

28

SignedThe minimal payload that can be supported would be 1 destination account. With payload:
0

31

63

95

127

159

191

Last Valid Hash

CounterTo

AmountCounter

223

255

us










Fee

Signed










From
Signature 1 of 2
Signature 2 of 2
With payload the minimum size: 176 bytes

The Proof of History sequence packet contains the current hash, counter,and the hash of all the new messages added to the PoH sequence and thestate signature after processing all the messages. This packet is sent once
every N messages are broadcast.
Proof of History packet:
0

31

63

95

127

159

191

223

255

CounterCurrent Hash







Messages Hash
State Hash

SignedSignature 1 of 2
Signature 2 of 2
Minimum size of the output packet is: 132 bytes

On a 1gbps network connection the maximum number of transactions

29

possible is 1 gigabit per second / 176 bytes = 710k tps max. Some loss 1−4%
is expected due to Ethernet framing. The spare capacity over the target
amount for the network can be used to increase availability by coding theoutput with Reed-Solomon codes and striping it to the available downstream
Verifiers.

7.3

Computational Limits

Each transaction requires a digest verification. This operation does not use
any memory outside of the transaction message itself and can be parallelized
independently. Thus throughput is expected to be limited by the number ofcores available on the system.
GPU based ECDSA verification servers have had experimental results of
900k operations per second [9].

7.4

Memory Limits

A naive implementation of the state as a 50% full hashtable with 32 byte entries for each account, would theoretically fit 10 billion accounts into 640GB.
Steady state random access to this table is measured at 1.1 ∗ 107 writes orreads per second. Based on 2 reads and two writes per transaction, memory
throughput can handle 2.75m transactions per second. This was measured
on an Amazon Web Services 1TB x1.16xlarge instance.

7.5

High Performance Smart Contracts

Smart contracts are a generalized form of transactions. These are programs
that run on each node and modify the state. This design leverages extended
Berkeley Packet Filter bytecode as fast and easy to analyze and JIT bytecodeas the smart contracts language.
One of its main advantages is a zero cost Foreign Function Interface.
Intrinsics, or functions that are implemented on the platform directly, arecallable by programs. Calling the intrinsics suspends that program andschedules the intrinsic on a high performance server. Intrinsics are batched
together to execute in parallel on the GPU.
In the above example, two different user programs call the same intrinsic.
Each program is suspended until the batch execution of the intrinsics is30

Figure 11: Executing BPF programs.

complete. An example intrinsic is ECDSA verification. Batching these calls
to execute on the GPU can increase throughput by thousands of times.
This trampoline requires no native operating system thread context switches,
since the BPF bytecode has a well defined context for all the memory that
it is using.
eBPF backend has been included in LLVM since 2015, so any LLVM
frontend language can be used to write smart contracts. Its been in theLinux kernel since 2015, and the first iterations of the bytecode have beenaround since 1992. A single pass can check eBPF for correctness, ascertain
its runtime and memory requirements and convert it to x86 instructions.

References[1] Liskov, Practical use of Clocks
http://www.dainf.cefetpr.br/ tacla/SDII/PracticalUseOfClocks.pdf[2] Google Spanner TrueTime consistency
https://cloud.google.com/spanner/docs/true-time-external-consistency[3] Solving Agreement with Ordering Oracles
http://www.inf.usi.ch/faculty/pedone/Paper/2002/2002EDCCb.pdf

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[4] Tendermint: Consensus without Mining
https://tendermint.com/static/docs/tendermint.pdf[5] Hedera: A Governing Council & Public Hashgraph Networkhttps://s3.amazonaws.com/hedera-hashgraph/hh-whitepaper-v1.0-180313.pdf[6] Filecoin, proof of replication,
https://filecoin.io/proof-of-replication.pdf[7] Slasher, A punative Proof of Stake algorithm
https://blog.ethereum.org/2014/01/15/slasher-a-punitive-proof-of-stake-algorith[8] BitShares Delegated Proof of Stake
https://github.com/BitShares/bitshares/wiki/Delegated-Proof-of-Stake[9] An Efficient Elliptic Curve Cryptography Signature Server With GPU
Acceleration
http://ieeexplore.ieee.org/document/7555336/[10] Casper the Friendly Finality Gadget
https://arxiv.org/pdf/1710.09437.pdf

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