by
piotr janiuk
golem: Three sided market
INTRODUCTION
challenges of decentralized setting
Gametheoretical standpoint for all actors
01
Trust in the network
02
INTRODUCTION
Strict verification requirement
03
Nondeterministic computations
04
https://truebit.io  can be used in case of deterministic computations
Monte Carlo methods
why rendering?
source: https://www.behance.net/gallery/21615831/Testingrenderengines
INTRODUCTION
The domain of the problem is well known
CG is present in most media, and pleasing results are Achieved fairly quickly
trust and security
INTRODUCTION
Concent
01
Intel SGX technology
02
 Is a service in the Golem network, which aims to improve the integrity and security of transactions

Technology which can be used to implement secure remote computation
a brief intro to golem tech
Golem Technology
and Nondeterminism
first poc
Minilight (Python)
GOLEM TECHNOLOGY
Focus on computations / computationcentric use case
Specific renderer hardcoded in provider
(POC 0.5  Minilight)
01
first poc
pbrt (C++)
GOLEM TECHNOLOGY
Focus on computations / computationcentric use case
 Trusted computing nodes

Lack of transaction framework

No payments
Rendering as a separate task
02
Basic protocol
03

Rendering task is abstracted out and decoupled from the Golem client code. The first implementation used PBRT renderer as a rendering backend
first poc
First POC
01
Intranet computations
02
GOLEM TECHNOLOGY
TASK POV
Execution environment:
 Multiple computational backends
 Multiple operating systems
 Multiple hardware configurations
01
High level task definition
03
 Timeouts (global, local)

Task splitting configuration
Subtask results collector
Task configuration:
02
04
GOLEM TECHNOLOGY
computation environment
GOLEM TECHNOLOGY
Physical infrastructure: Heterogenous p2p network
01
Logical components: Requestors, providers, (optional) concents
02
computation environment
GOLEM TECHNOLOGY
Task sending and resource distribution
03
Verification
04
security considerations
GOLEM TECHNOLOGY
Provider security
 Isolating host from possibly malicious software
 Accepting only valid tasks
01
Requestor security
02
 Trust (i.e. reputation)

Results verification
 Keep track of providers and notion of trust
Infrastructure security
03

no unauthorized internet access (e.g. botnet protection)
Infrastructure and actors
GOLEM TECHNOLOGY
New topology and requirements – P2P
01
Distribution of execution environments
02
Notion of trust – reputation of the actors
03
New integrations – new usecases
04
Actors in the network
05
Incentives and economy
GOLEM TECHNOLOGY
Gametheoretical conditions for all actors
01
Requestor incentives
02
Provider incentives
03

Notion of trust in the network (local and global schemes)

Known verification requirements and algorithms

Open market

Wants to compute cheap, fast and get a highquality result

Wants to compute fast and make the highest possible profit
payments inside the network
GOLEM TECHNOLOGY
Reward scheme:
01

Every integration can use any custom reward scheme, as long as it is compliant with the transaction framework

Key parties are:

task creators (Requestors): how they pay for computations and software

Providers: how they are rewarded (eg. per hour? per task?)

Developers: payment for software (free? donation? per node? per use? per CPU hour


Ethereum network is used to transfer value and information between the peers
a brief intro to golem tech
Golem Technology
and Nondeterminism
first usecase (mc photorealistic rendering)
Sources of nondeterminism
NONDETERMINISM
Random number generators
01
02
Floating point encoding standards on different platforms:
 CPUs
 GPUs
Sources of nondeterminism
NONDETERMINISM
Random number generators
01
02
Float encoding standards
on different platforms:
Multithreading:

Regular CPU multithreading
03
 CPUs
 GPUs
Sources of nondeterminism
NONDETERMINISM
Random number generators
01
02
Float encoding standards
on different platforms:
Multithreading:

Regular CPU multithreading

SIMT or SMT in the case of GPUs
03
 CPUs
 GPUs
cuda architecture
Sources of nondeterminism
NONDETERMINISM
Random number generators
01
02
Float encoding standards
on different platforms:
Multithreading:

Regular CPU multithreading

SIMT or SMT in the case of GPUs
03
 CPUs
 GPUs
Nondeterministic implementation
of MC methods
04
problems with nondeterminism
01
Rendering
NONDETERMINISM

Deterministic input

Nondeterministic computation

Nondeterministic results
problems with nondeterminism
Machine Learning
02

Deterministic or randomized input

Deterministic or nondeterministic computations

Nondeterministic results
NONDETERMINISM
problems with nondeterminism
Proof of Work

Deterministic input

Nondeterministic amount of work required

Deterministic and efficient verification

The exact amount of performed computations is hard to verify
03
NONDETERMINISM
Nondeterminism and consensus
01
NONDETERMINISM
Nondeterministic state

Nondeterministic results cannot be directly compared

Each node may see a slightly different result

A similarity measure is required
Nondeterminism and consensus
Golem's setting

Consensus between a very small (usually two) number of nodes

Means of dealing with a dispute are implemented
02
NONDETERMINISM
monte carlo methods
Introduction
Basic probability
Probabilistic space
01
Probabilistic measure via density function
02
probabilistic space
– domain
– probabilistic measure
probability density function
MONTE CARLO METHODS
Basic probability
Random variable
03
random variable
MONTE CARLO METHODS
”
A random variable, usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types of random variables, discrete and continuous.
– Valerie J. Easton and John H. McColl's Statistics Glossary v1.1
Basic PROBABILITY
Expected value
04
Continuous random variable expected value
definition
Linearity of random variables with regards to E
sum of i.i.D. random variable
MONTE CARLO METHODS
Variance
05
Basic PROBABILITY
Vector of random variables
05
Sample mean
Expected value of sample mean
Variance of sample mean
Standard deviation
$$P(\mathrm{E}X = \lim_{N \rightarrow \infty} \frac{1}{N} \sum_{k=1}^{N}X_k) = 1$$
Strong law of large numbers
Strong law of large numbers
06
MONTE CARLO METHODS
monte carlo methods
”
Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results.
Can be used to solve any problem having a probabilistic interpretation.
By the law of large numbers, integrals described by the expected value of some random variable can be approximated by taking the empirical mean of independent samples of the variable.
– wikipedia
MONTE CARLO METHODS
approximation of using MC methods
$$\pi$$
monte carlo integration
Goal

Find the best possible approximation of the integral by sampling the integrated function at a finite set of points
01
Integral
MONTE CARLO METHODS
$$\int_{S}f(x)dx$$
Expected Value approximation
02
Average with respect to some density p
$$\mathrm{E}(f(x)) = \int_{\Omega} f(x)p(x) dx \approx \frac{1}{N} \sum_{k=1}^{N}f(x_k) $$
Average estimation  uniform samples
$$P(\mathrm{E}X = \lim_{N \rightarrow \infty} \frac{1}{N} \sum_{k=1}^{N}X_k) = 1$$
Strong law of large numbers
$$\mathrm{E}(f(x)) = \int_{\Omega} f(x)dP(x) = \int_{\Omega} f(x)p(x) dx $$
monte carlo integration
Mean of a function
03
An average expressed as an expected value with uniform density function
Integral via mean
N uniformly distributed samples
$$\mu(\Omega) I = \mu(\Omega) \mathrm{E}(f(x)) \approx \mu(\Omega) \frac{1}{N} \sum_{k=1}^{N}f(x_k)$$
Integral Estimator
MONTE CARLO METHODS
Integral estimation using mean value
04
monte carlo integration
Integral expressed in terms of an arbitrary distribution
05
Average with respect to some density p
expected value of a slightly modified function
$$\int_{\Omega}f(x)dx = \int_{\Omega} \frac{f(x)}{p(x)}p(x) dx \approx \frac{1}{N} \sum_{k=1}^{N}\frac{f(x_k)}{p(x_k)}$$
Integral estimation  i.i.D. samples sampled from the distribution specified by P
where
independent identically distributred
a vector of N i.i.d. variables
MONTE CARLO METHODS
$$\mathrm{E}(f(x)) = \int_{\Omega} f(x)dP(x) = \int_{\Omega} f(x)p(x) dx $$
$$\mathrm{E}(\frac{f(x)}{p(x)}) = \int_{\Omega} \frac{f(x)}{p(x)} p(x) dx = \int_{\Omega} f(x) dx$$
monte carlo integration
Standard deviation
Problem of diminishing return
06
MONTE CARLO METHODS
”
To halve the error the number of samples has to be quadrupled.
Variance reduction techniques
07

Choosing density wisely we can greatly reduce initial variance (Importance Sampling)
monte carlo integration
Standard deviation
Problem of diminishing return
06
MONTE CARLO METHODS
”
To halve the error the number of samples has to be quadrupled.
Variance reduction techniques
07

Domain partitioning (Stratified Sampling)
the rendering equation
rendering equation
”
In computer graphics, the rendering equation is an integral equation in which the equilibrium radiance leaving a point is given as the sum of emitted plus reflected radiance under a geometric optics approximation.
– wikipedia
RENDERING EQUATION
radiometric quantities
Energy
01
Energy
Flux
02
Radiance
03
Flux
radiance
$$\theta$$
RENDERING EQUATION

Radiant flux per unit solid angle per unit projected area

Intuitively, radiance expresses how much power (flux) arrives at (or leaves from) a certain point on a surface, per unit solid angle, and per unit projected area

Time rate of flow of radiant energy

This quantity expresses how much total energy flows from/to/through a surface per unit time

Energy of a collection of photons
radiance propagation
Bidirectional Reflectance Distribution Function (BRDF)
 A function that defines how light is reflected from a surface
01
Reflected radiance
02
BRDF
differential representation
integral form
RENDERING EQUATION
$$x$$
rendering equation
Outgoing radiance
01
Rendering equation
02
Outgoing radiance
Rendering equation
RENDERING EQUATION
Operator formulation
03
integral form in operator notation
operator defined as
rendering equation
Neumann Series
04
neumann series expansion
$$T = \sum_{k=0}^{\infty} T^kL_e$$
Compact notation

Renderers  black box engines

The rendering equation expressed via Neumann series expansion suggests that the final solution can be composed of independently computed results.

These partial results are computed by renderers, which can be treated as black box computation engines

RENDERING EQUATION
rendering process
RENDERING EQUATION
”
Rendering or image synthesis is the automatic process of generating a photorealistic or nonphotorealistic image from a 2D or 3D model (or models in what collectively could be called a scene file) by means of computer programs. Also, the results of displaying such a model can be called a render. A scene file contains objects in a strictly defined language or data structure; it would contain geometry, viewpoint, texture, lighting, and shading information as a description of the virtual scene. The data contained in the scene file is then passed to a rendering program to be processed and output to a digital image or raster graphics image file.
– wikipedia
”
(in this context) a computer program used to generate photorealistic images from a scene file (consisting of 3D models and description of materials and lights)
rendering
renderer
path tracing
RENDERING EQUATION
Path formulation
01

Markov Chain approach

Independent evaluation of each path
examples of path grouping
RENDERING EQUATION

Each tile is rendered independently, the final image is composed of tiles rendered with full quality

Multiple, independent full resolution images are rendered. Each one uses only a fraction of samples per pixel as compared to the final result. The final image is obtained by means of averaging these intermediate results
monte carlo methods
Verification challenges
Verification challenges
VERIFICATION CHALLENGES
Gametheoretical approach
01
Verification process outline
02

Each rational participant wants to maximize his/her benefit

1st step  automatic verification

Classifier can be easily replaced (it is a plugin architecture)

The main classifier can consist of multiple classifiers and image comparison algorithms


2nd step  manual verification and acceptance/rejection
hybrid approach
Verification challenges
VERIFICATION CHALLENGES
Automatic verification
03

Outlier removal method

Verification based on redundant computations (probabilistic)
outlier removal method
verification based on redundant computations
Verification challenges
VERIFICATION CHALLENGES
Manual verification
04

The final result may be accepted or rejected manually
manual Verification
consequences of decentralization
consequences of decentralization
Requestor prepares scenes and sends them together with the resources to a render farm
01
Render farm splits the task and distributes computations between nodes
02
Nodes compute partial results which are composed into final images/animations
03
The results are sent back to the requestor
04
default approach
DECENTRALIZATION
consequences of decentralization
decentralized setting
Requestor wants a valid result for the lowest price possible in a specified timeframe:

Must carefully set the number of samples for a valid result to avoid unnecessary computation

Won't pay for invalid results

Requires fair protocol in case of invalid results
01
DECENTRALIZATION
consequences of decentralization
decentralized setting
Provider wants to earn as much as possible for the least possible resources provided:

Can try to generate the image of lesser quality and use fewer samples than the requestor requested

May not be paid for valid results and needs to have a way of recovering the payment

Protocol must be fair
02
DECENTRALIZATION
verification in The case of monte carlo rendering
verification  monte carlo rendering example
VERIFICATION  MC
Blender (Cycles renderer) usecase
01

Bitbybit verification is not viable

Each tile has to be either marked as valid or invalid by automatic verification algorithm
verification  monte carlo rendering example
VERIFICATION  MC
Example of classifier implementation

Local verification

Sample rectangles are rendered with the same parameters as the main tile

Each sample must match the corresponding rectangle in the tile

Total area of sample rectangles is only a small fraction of the tile area

02
automatic Verification
verification  monte carlo rendering example
VERIFICATION  MC
Example of classifier implementation

Redundancy

Rendering each tile multiple times and comparing

Rendering redundantly but not necessarily duplicating all pixels

Multiple schemes are possible, which makes it harder to cheat

03
verification  monte carlo rendering example
VERIFICATION  MC
It is probabilistic
 Tunable redundancy

Tunable verification thresholds

Tunable trust requirements (e.g. when SGX nodes are present)
01
summary
Classifier only assists
02
It is adjustable
03
image comparison and verification
image comparison and verification
COMPARISON AND VERIFICATION
Formal problem statement
01
Verification prerequisites
 For the same input parameters and renderer run in exactly the same conditions we have
Approach 1  estimate the probability
provided that we know
input bitmap
renderer
unknown parameters used to render
parameters specified by the requestor
APPROACH 2  ESTIMATE THE PROBABILITY (preferred solution)
provided that we know
but we do not want to recompute the whole
Estimation
02
image comparison and verification
COMPARISON AND VERIFICATION
Example approach
02
transform

Two transforms

Wavelet transform

Edge enhancing transform


Two measures

SSIM (structural similarity)

MSE (mean squared error)

Similarity measure
Similarity of images
01

a decent algorithm is required to help the classifier decide if two bitmaps are similar enough to be treated as the result of the same computation
image comparison and verification
COMPARISON AND VERIFICATION
Picture w/ no transform
Importance of the transforms
03
Comparison of two bitmaps

Picture w/ no transform vs Picture w/ edge enhancing transform (one SPP difference in images)
image comparison and verification
COMPARISON AND VERIFICATION
Picture w/ edge enhancing transform
Importance of the transforms
03
Comparison of two bitmaps

Picture w/ no transform vs Picture w/ edge enhancing transform (one SPP difference in images)
other usecases
COMPARISON AND VERIFICATION
Machine Learning
01

Golem can be used to find the best set of hyperparameters for a given model

Hyperparameters describe network structure, eg. number of layers, number of nodes in each layer. They are set before training

Each provider is doing a full training of a neural network but for different values of hyperparameters

Requestors can use different algorithms for searching parameter space, eg. grid search, random search, bayesian optimization search

Each dot in the picture represents one set of parameters, ie. one network trained by Provider
other usecases
COMPARISON AND VERIFICATION
Machine Learning  Verification
02

Neural network training is a sequential algorithm

Feeding sets of batches to the network is one iteration step. After every step, the provider computes the hash of the network state and sends it to requestor

Requestor asks provider for randomly chosen states and verifies that transition to the next state was done correctly

Each rectangle represents one network state. The Requestor checks the transition between randomly chosen states
other usecases
COMPARISON AND VERIFICATION
Proof of Work
01
Nonces for PoW
$$S_1$$
$$S_K$$
$$\bigcup_{i=1}^{K}S_i = S$$
$$S_i \cap S_ j= \emptyset$$
$$i \neq j$$
for
$$\ldots$$
PoW targets
PoW input string
PoW hash function
PoW target
PoW intermediate target (used during verification),
concent
what is concent?

Providers should be paid for calculations they do

Requestors must get proper results (honest calculations)

If something goes wrong, an optional third party may be utilized to solve the dispute
what is concent?

It is a web service (which ultimately may be distributed)

It is a special type of node in the Golem Network

Its purpose is to improve the fairness of transactions

It is passive

It is optional
CONCENT
what concent does
Enforcing communication:

After a Requestor gives a task to a Provider and the Provider starts to compute, the communication must follow the protocol. If one party goes offline or rejects accepting proper messages, Concent can enforce the right communication, or ensure that the malicious party takes responsibility for the broken communication
01
CONCENT
what concent does
Additional verification:

Verification is performed by Requestors. If a Requestor rejects the correct result, the Provider calls Concent which performs additional verification. It is similar to the one performed by the Requestor but stronger. If the result turns out to be correct then Concent forces the Requestor to pay
02
CONCENT
what concent does
Enforcing payment:

If a Requestor does not pay on time for accepted results, then Concent will charge payments from the Requestor's deposit
03
CONCENT
how concent works
Requestor sends a task to a Provider
01
Provider calculates the task and sends the result to the Requestor
02
Requestor verifies and accepts the result
03
If the result is correct, the Requestor pays, if not they do not pay for it
04
communication in golem
CONCENT
Concent's role in solving conflicts
All Concent usecases are meant to overcome flaws like attacks/frauds
01
Usecases can be initialized by one or two parties
02
Usecase starts when a party does not receive a response, is not paid on time, or if their results are wrongly rejected
03
To use Concent, parties must agree to it at the beginning by putting deposits in GNT which are transferred to a dedicated Ethereum contract
04
Concent has special permissions on deposits
05
Concent is not a free service
06
CONCENT
why concent?
can fairness in transactions between requestors and providers be ensured?
Fairness means that Requestors do not pay for incorrect results and must always pay for correct results
01
Main options for ensuring fairness in transactions:

Use trusted thirdparty

Use blockchain and/or consensus in network
02
Verification is critical for defining Concent and nonConcent solutions
03
CONCENT
is concent reliable?
A Priori assumption: Providers and Requestors trust Concent
01
Under normal conditions Concent is called very rarely
02
Concent as a service is a singlepointoffailure and may be vulnerable to DDoS attacks
03
The principle is that Golem Network can calculate tasks even if Concent is down
04
Concent service will be more decentralized in the future
05
It suffices for Concent to be up 99% of time
06
CONCENT
can concent be decentralized?
In the future we plan to allow others to run valid Concent services (i.e. software developers may want to run Concent for their own applications)
01
Technologies like SGX provide trustworthy and noncontrolled calculations that are considered decentralized and i.e. can perform additional verification
02
CONCENT
SGX TECHnology
sgx technology
SGX TECHNOLOGY
”
SGX is a set of CPU instructions and hardware platform enhancements that enable apps to create private areas within which code and associated data can be stored safe from compromise during execution. If used correctly, this protection can prevent compromise due to attacks from privileged software and many hardware attacks
– intel
sgx technology
Verifiable computation:

Prevents BIOS/OS/VMM/SMM/drivers attacks

Prevents bus snooping and memory tampering

Provides hardware measurement mechanism

Provides hardware attestation (local and remote)

Provides hardware sealing (to the enclave and to the author)
02
Example usages:

User authentication

Sensitive data processing

Verifiable computation
01
SGX TECHNOLOGY
– verifiable computation is PARTICULARLY important from golem's perspective
sgx technology
Enclaves:

Similar in spirit to a DLL, SO or Dynamic Library, plus some additional configuration data

Limited to computations only (no direct IO or OS calls allowed)

IO or OS calls available by means of OCALLs mechanism (configured statically)

Enclave

Run and hosted in an untrusted environment

Once instantiated it becomes a trusted part of the app (As there may appear attacks on SGX which cannot be foreseen today)

03
SGX TECHNOLOGY
TCS – THREAD CONTROL STRUCTURE
lifetime of an enclave
Enclave creation:

Binary payload (similar to a dll) which is run inside the enclave

Configuration data:

Stack size

Heap size

Threads (via Thread Control Structures)

Enclave author’s public key

Software version

Product id

01
Enclave Launch:

Currently launched by Launch Enclave provided by Intel
02
Enclave Usage:

Communication via ECALLs and OCALLs
03
intel sgx application
SGX TECHNOLOGY
Actors/Parties
ISV
01
Application
02
Application SGX Enclave
03
Intel IAS
04
SGX TECHNOLOGY

Enclave developer; assists in attestation; for now must be registered to Intel

Launches enclave and provides access to it; untrusted

Provides measurement and mrsigner for attestation and sealing; trustworthy

Intel Attestation Service  used to verify EPID during remote attestation
security related activities
Measurement
01
Attestation
02
Sealing
03

mrenclave – hash of vanilla enclave state including its security properties – used to prove that the enclave was instantiated correctly

Local – between enclaves on a single machine

Remote – to prove the enclave validity to a remote user/client

Used to securely store persistent data

Seal to current enclave – only the enclave with the matching measurement can unseal the data

To the author – used to unseal data in enclaves developed by the same author
local attestation
remote attestation
SGX TECHNOLOGY
cons of SGX TECHNOLOGY

Intel presence is required to attest quotes and partially to launch enclaves

Launching an enclave with all SGX features enabled requires Launch Enclave provided by Intel

Currently the Intel Attestation Service (IAS) is necessary for the remote attestation to work


ISV requires Intel approval

Remote attestation procedure does not require IAS by design and a perfectly valid remote attestation process without IAS can be implemented (once IAS is liberated)
SGX TECHNOLOGY
SGX and golem
SGX and golem
How SGX can be used to the advantage of Golem
01

Software Developer is ISV and currently can serve as attestation service  and connect to Intel to assure correctness of the enclave in question

Once a proof for an enclave is created by IAS, the proof can be subsequently verified offline

The protocol used to communicate with IAS is not critical from a security standpoint


Requestor and Concent are "hosting" Developers code
SGX AND GOLEM
SGX and golem
How SGX nodes can be added to Golem
03

Additional Concent use cases
 Even though SGX nodes are present and arbitrage is not as necessary as it was, additional logic related to licensing models may still be required and should be implemented in a decentralized and secure manner
SGX AND GOLEM

When (if) SGX is liberated, then only ISV is required to have a high reputation (from requestors' point of view)

ISV has skin in the game to provide a valid enclave, so they should be trusted with attesting enclaves
SGX in Golem  possible future
02
SGX and golem
How SGX nodes can be added to Golem
03

The most resource consuming aspect of Concent is additional verification

Concent can delegate verification to SGX nodes increasing scalability and keeping the same level of trustworthiness. It may even happen that SGX nodes computations are considered as more trustful. This makes the most sense if SGX nodes compute noticeably slower than regular nodes

In such a setting a regular node may not be incentivized to call SGX nodes directly, but there would be a special case where an SGX node would be able to provably repeat computation and compare results with questionable ones


Other solutions

May encompass sharing logic between one group of nodes and the state and history on other nodes

SGX AND GOLEM
SGX and golem
Computations and Verification with SGX
04

We can run the whole computation inside an SGX enclave (if possible) and if the result is returned  it is the result of a valid computation and no verification is needed

The aforementioned approach may result in less security guarantees than the default SGX enclaves, but should still be much more secure than computations carried out the regular way

SGX AND GOLEM
SGX and golem
Computations and Verification with SGX
04

We can run the whole computation inside an SGX enclave (if possible) and if the result is returned  it is the result of a valid computation and no verification is needed
 The aforementioned approach may result in less security guarantees than the default SGX enclaves, but should still be much more secure than computations carried out the regular way

SGX enclaves are restricted to computations only, but more general platforms allowing arbitrary computations inside the enclave are being researched and developed. We closely cooperate with Invisible Things Labs, the team/company solving problems in this exact area
SGX AND GOLEM
Graphene: example approach for the universal solution
SGX and golem
How SGX can help with verification
05

We can use SGX nodes and redundancy to help verify (with tunable probability) the results from regular nodes

Just as regular redundancy, but we know that results from SGX nodes are significantly more trustworthy and can be used as reference data


Additional verification logic can be implemented and run in an enclave whereas the main computation is performed on the regular host machine, but with intermediate results being sent to the Enclave

Performance

SGX supports EPC paging (enclave's private memory protected from the host and external, physical attacks), but it may be harmful to the performance

Depending on the performance impact of SGX, an appropriate fraction of calculations should be delegated to SGX nodes, the less impact, the more subtasks can be sent to SGX nodes (possibly for higher price)

SGX AND GOLEM
SGX and golem
Summary
01

In a perfect world SGX or similar technology:

Should provide mechanism for verifying that declared computations indeed took place

Should assure privacy of sensitive data

Should not impose large computational overhead

Requestors shouldn’t have to verify the results locally at all


SGX or similar technology can be used to help decentralize server solutions in a secure manner

The Golem network may consist of regular nodes, nodes with SGX and Concents using SGX

NonSGX nodes are first class citizens but may require more mechanisms to assure valid results produced by them (e.g. local verification, redundant computations, reputation)


Such technology seems to be a perfect match for nondeterministic computations (as defined in previous sections)
SGX AND GOLEM
summary
summary
 There is much more to Golem than verification of broadly defined nondeterministic tasks, but this seems to be one of the most prominent challenges in a decentralized and trustless setting
 A detailed introduction to Monte Carlo rendering was presented to give you a better understanding of the problems one has to tackle when fully a decentralized infrastructure is to be prepared. As most of the issues are well known, applying them in a trustless decentralized setting requires additional thoughts in order to balance: quality, security, pricing, and efficiency
 Our approach to this challenge was presented and we shared a few ideas and directions we’re already exploring in regard to improving general network security without sacrificing too much efficiency
 A few approaches to verification of rendering tasks were shown along with the introduction of Concents, optional infrastructural component purposed with securing the network and allowing software developers to implement custom licensing logic
 Then Intel SGX technology was shown as a potentially good fit with regard to both nondeterministic verification and decentralization of Concent
SUMMARY
Piotr janiuk
CTO, cofounder
Golem’s speaker at Devcon0 and Devcon1. Previously a designer and lead developer of Black Vision, a realtime rendering engine for TV broadcasting. Implemented the fastest (at the time) software jpeg2000 codec for DCP. Also took part in a few side projects related to p2p networks.
Interested in computer graphics, digital signal processing, cryptography, compilers, virtualization, blockchain tech, parallel computing, distributed computing, trustless computing and optimization techniques.
viggith@golem.network
https://www.linkedin.com/in/viggith/
thank you
www.golem.network
For the help with this presentation thanks to:
Substantial knowledge: Aleksandra Skrzypczak, Łukasz Gleń
Design: Jacek Muszyński, Urszula Trzaskowska (Cobaltblue)
Edit: Joanna Janiuk, Dan Horne
Golem Deep Dive
By The Golem Project