A Primer on Cache Coherence Protocols

In modern multi-core processors, multiple cores operate in parallel, each with its own cache to store frequently accessed data. These caches are designed to reduce the time it takes for a processor to retrieve data from memory, speeding up overall performance. However, as multiple cores work on the same data, problems arise when changes made by one core are not immediately visible to others. This discrepancy can lead to inconsistencies, incorrect computations, and even system failures. This is where cache coherence comes into play.

Cache coherence is required to ensure that all processors in a multi-core system have a consistent view of shared data. Without coherence mechanisms, one core could modify a value in its cache while another core continues to use an outdated version of the same data from its own cache, leading to conflicting operations. For example, if one processor updates a variable but doesn't inform the other processors of the change, those processors will continue working with the old, incorrect value. This leads to problems in the program's execution, violating the fundamental principle of data consistency.

Moreover, maintaining coherence is especially critical in multi-core systems because the use of independent caches means that memory can be both modified and accessed simultaneously by different processors. These caches are invisible to each other unless there is a mechanism to synchronize them. Without such synchronization, a system would suffer from anomalies like "stale reads" (where a processor reads an outdated value) or "lost writes" (where a processor overwrites a value that was updated by another processor but never observed). This would make it extremely difficult to write correct multi-threaded programs or maintain correct results in shared-memory systems.

Cache coherence protocols ensure that any changes made to a shared data item by one processor are properly propagated to all other caches that hold that data. These protocols maintain consistency across all caches by defining rules for how data is propagated, when caches should be updated or invalidated, and how conflicts between processors trying to access the same data are resolved. Without cache coherence, the benefits of multi-core processing would be significantly reduced, as inconsistencies in shared data would lead to incorrect and unpredictable results.

The evolution of cache coherence protocols reflects the need to manage data efficiently across multiple cores and caches, reducing unnecessary traffic, minimizing delays, and ensuring that caches have the most recent version of data. As we progress from the simpler MSI protocol to the more sophisticated MOESIF, new states and transitions are introduced to optimize specific situations, such as private data handling, shared modified data, and shared read-heavy workloads. To understand this progression fully, it’s essential to differentiate between CPU requests (e.g., read or write operations by a processor) and bus requests (e.g., coherence traffic between caches and memory to maintain consistency). These signals come from different sources and serve different purposes in the protocol.

MSI Protocol: The Basics

The MSI protocol is one of the most basic cache coherence protocols, employing three states: Modified (M), Shared (S), and Invalid (I). In this protocol, the states manage data consistency between caches and memory but come with significant overhead for data sharing and modification.

• Modified (M): The cache line is locally modified and is inconsistent with main memory. No other cache holds a valid copy, and the memory is stale.

• Shared (S): The cache line is valid, can be shared by multiple caches, and matches the data in memory. The memory holds the correct data.

• Invalid (I): The cache line is not valid in the cache, and the processor must fetch the data from another cache or from memory.

CPU requests such as reads and writes initiate actions in the cache, which may require communication with other caches via bus signals.

• When a processor reads from a cache line that is in the Invalid (I) state, a BusRd (Bus Read) signal is issued by the CPU. This is a bus request sent to retrieve the data, either from memory or another cache. If other caches hold the data in the Shared state, they will supply the data, and the cache line in the requesting cache transitions to Shared (S). If no other caches hold the data, it is fetched from memory and transitions to Shared if it's only read, or to Modified (M) if a write will follow.

• When a processor writes to a cache line that is in the Invalid (I) or Shared (S) state, it issues a BusRdX (Bus Read Exclusive) signal, meaning the cache must retrieve the data in exclusive mode and invalidate any other copies in other caches. This request ensures that no other cache holds the line in Shared anymore, and the requesting cache transitions to Modified (M). The BusRdX ensures that the processor now has exclusive access to the data for writing.

• The BusRdX is called "exclusive" because it not only reads the data but also ensures that the requesting processor is the only one allowed to modify it, invalidating copies in other caches.

• BusUpgr (Bus Upgrade) is another bus signal used when a cache line is already in the Shared state and the processor wants to write to it. Since the cache already has the data, the cache doesn’t need to fetch it again, but the BusUpgr signal is issued to invalidate the line in other caches, upgrading the line from Shared (S) to Modified (M) in the current cache.

In MSI, the inefficiencies become clear when data is frequently shared and written to, requiring invalidations during writes and write-backs to memory when data is shared between caches. The simplicity of MSI results in high coherence traffic, especially in workloads where data is shared and modified frequently.

MESI Protocol: Optimizing Private Data Handling

The MESI protocol improves upon MSI by adding a fourth state: Exclusive (E). This state optimizes situations where a cache holds data exclusively, allowing for more efficient data modification without unnecessary invalidations. The key idea is that data can be exclusively owned by a single cache without any need for other caches to be invalidated during a write, provided no other caches hold the data.

• Exclusive (E): The cache line is valid and matches the data in memory, but no other caches hold it. The cache can transition the data directly from Exclusive to Modified (M) if the processor writes to it, without issuing any bus requests. This eliminates unnecessary bus traffic for private data modifications.

When a processor reads a cache line and no other cache has the data, the cache line enters the Exclusive (E) state. If the processor writes to the line later, it transitions directly from Exclusive to Modified, avoiding any bus transactions because the data is already exclusive to this cache. This reduces unnecessary BusRdX or BusUpgr requests that were prevalent in MSI for similar cases.

When another processor issues a BusRd to a cache line in the Exclusive state, the cache responds with the data, and the line transitions to Shared (S) in both the reading cache and the cache that held it exclusively. This allows for efficient read-sharing, while still maintaining control over when the data transitions to a shared state.

MESI optimizes for cases where the data is privately held by a single cache. This reduces invalidation traffic and improves performance when a cache can modify its data without needing to communicate with others. However, MESI still has inefficiencies when it comes to handling modified data that needs to be shared across caches, as it still requires a write-back to memory before the cache line can be shared.

MOSI Protocol: Addressing Write-Back Inefficiency

The MOSI protocol introduces the Owner (O) state to reduce the number of memory write-backs needed when modified data is shared between caches. In MESI, when a cache line is in the Modified state and another cache requests the data, the modified data must be written back to memory before it can be shared. The MOSI protocol solves this by allowing the modified data to be supplied directly by the cache holding the most recent version, without requiring a write-back to memory.

• Owner (O): The cache line is shared with other caches, but this cache holds the most recent (dirty) version of the data. The memory is out-of-date, but the cache holding the Owner state is responsible for supplying the data to other caches. This reduces the need to write modified data back to memory.

In MOSI, if a processor reads a cache line that is modified in another cache, the cache holding the Modified line transitions to the Owner (O) state, and the requesting cache enters the Shared (S) state. The Owner cache supplies the data directly to the requesting cache without writing it back to memory first. This improves performance in shared modified data scenarios, where frequent memory write-backs would otherwise slow down the system.

The Owner state reduces the overhead of write-backs, especially in cases where multiple caches need access to modified data. Instead of writing modified data back to memory when sharing it, the cache that modified the data simply transitions to Owner and keeps supplying it as long as other caches request it.

MOESI Protocol: Combining the Best of MESI and MOSI

The MOESI protocol merges the benefits of both MESI and MOSI, combining the Exclusive and Owner states to handle both private and shared modified data more efficiently. It introduces flexibility in dealing with both scenarios.

In MOESI, if a cache line is held privately, it can reside in the Exclusive (E) state, allowing the processor to modify it without any coherence traffic, as in MESI. If the data becomes shared and modified, the cache transitions to the Owner (O) state, optimizing the case where the most recent version of the data needs to be shared across caches. Other caches holding the line move to the Shared (S) state, and the cache holding the Owner (O) state supplies the data.

This dual mechanism allows MOESI to handle both private and shared data efficiently. For private data, the Exclusive state prevents unnecessary invalidations. For shared modified data, the Owner state eliminates unnecessary memory write-backs, reducing overhead in read-sharing scenarios.

MESIF Protocol: Optimizing Read-Heavy Workloads

The MESIF protocol is designed to optimize systems where data is read frequently across multiple caches. It adds the Forward (F) state, which designates one cache to respond to read requests for shared data, reducing bus contention when multiple caches hold the same cache line in the Shared (S) state.

• Forward (F): The cache line is shared, but only this cache is responsible for supplying data in response to read requests. Other caches holding the line in Shared remain passive and do not respond.

When a cache line is read by multiple processors and resides in the Shared state across multiple caches, only the cache in the Forward (F) state responds to future BusRd requests. This reduces the number of responses to read requests, minimizing bus traffic and improving efficiency in read-heavy workloads.

MESIF is particularly beneficial in situations where multiple caches frequently read the same data, as it prevents multiple caches from responding to every read request. By designating one cache to handle the responses, it minimizes unnecessary coherence traffic.

MOSIF and MOESIF Protocols: The Most Complete Solutions

The MOSIF protocol integrates both the Owner and Forward states to handle both read-heavy and write-heavy scenarios effectively. When data is frequently modified

and shared, the Owner state ensures that modified data can be shared without being written back to memory. When data is frequently read across multiple caches, the Forward state ensures that only one cache responds to read requests, reducing bus contention.

The MOESIF protocol represents the most sophisticated version of the cache coherence protocols, combining the Modified (M), Owner (O), Exclusive (E), Shared (S), Invalid (I), and Forward (F) states. In MOESIF, private data is handled efficiently with the Exclusive state, while shared modified data benefits from the Owner state, and read-heavy workloads are optimized by the Forward state. This allows MOESIF to handle the widest range of workloads with minimal coherence traffic and bus contention.

In MOESIF, a processor read from shared data leads to only the cache in the Forward state responding, while shared modified data can be supplied by the Owner cache without requiring a write-back to memory. This combination allows for maximum efficiency, but also comes with the highest level of complexity, requiring advanced cache management to handle the six states effectively.

State Transitions for All Protocols and Signals

Key:

• I: Invalid

• S: Shared

• M: Modified

• E: Exclusive

• O: Owner

• F: Forward

• BusRd: Bus Read (CPU Read Miss)

• BusRdX: Bus Read Exclusive (CPU Write Miss)

• BusUpgr: Bus Upgrade (CPU Write Hit in Shared)

This table summarizes the transitions for all discussed protocols based on different coherence events. It helps show how each protocol optimizes handling read and write requests while minimizing coherence traffic depending on whether it uses the Forward and Owner states.

In multi-core systems, managing cache coherence, atomicity, and memory consistency is crucial for ensuring that multiple processors working on shared data maintain a consistent and up-to-date view of memory. The interplay between cache coherence protocols, atomic operations like CAS and Test-And-Set, and memory barriers (also called fences) is essential to prevent race conditions and maintain sequential consistency in shared memory applications.

The Problem: Non-Atomic Coherence Messages

Cache coherence protocols like MESI, MOESI, or MESIF ensure that caches across multiple cores hold a consistent view of shared data. However, these protocols are not atomic when it comes to the propagation of coherence messages. For example, when a processor modifies a cache line, the cache may issue invalidate or write-back requests to other caches holding copies of the same data. These requests take time to propagate and be acted upon by other caches, and during this propagation period, other processors might still access stale data.

Consider a scenario where Processor A modifies a shared cache line and issues an invalidation message to Processor B's cache, which holds a shared copy of the line. During the time it takes for the invalidation to propagate and for Processor B to act upon it (by marking the cache line as Invalid (I)), Processor B could still read the stale data. The cache coherence protocol ensures that this inconsistency is eventually resolved, but during the brief window while coherence messages are in flight, there is a possibility for race conditions.

This non-atomic nature of cache coherence messages introduces the need for stronger synchronization mechanisms, especially in shared-memory applications where multiple processors are reading and writing to the same data concurrently.

Locks, Atomic Operations, and Memory Barriers: Ensuring Atomicity and Consistency

To address this challenge, locks, atomic operations, and memory barriers are used to provide stronger guarantees about atomicity and memory consistency in multi-core systems. Let’s break down how these mechanisms work in conjunction with cache coherence protocols to provide reliable synchronization.

1. Locks and Atomic Operations

Locks are used in multi-threaded programs to protect critical sections of code—parts of the program where shared data is accessed and modified. By enforcing mutual exclusion, locks prevent multiple processors from entering the critical section simultaneously and ensure that only one processor can modify shared data at a time.

Atomic operations like Compare-And-Swap (CAS) or Test-And-Set are used to acquire and release locks. These operations ensure that only one processor can successfully modify the lock at a time, preventing race conditions at the level of lock acquisition.

• Compare-And-Swap (CAS): CAS compares the current value of a memory location (e.g., a lock variable) with an expected value. If they match, it swaps the memory location's value with a new one atomically. This prevents other processors from acquiring the lock simultaneously.

• Test-And-Set: Test-And-Set checks if a memory location (e.g., the lock variable) is 0 (unlocked) and sets it to 1 (locked) atomically. Only one processor can successfully set the lock at a time.

These atomic operations are atomic at the level of the lock itself. When a processor acquires a lock using CAS or Test-And-Set, the cache coherence protocol ensures that the processor gets exclusive access to the memory location (lock variable). However, these operations do not guarantee that all coherence messages related to the shared data inside the critical section have been fully propagated. This is where memory barriers come into play.

Example: Lock Acquisition with CAS

// Example of acquiring a lock using Compare-And-Swap
void acquire_lock(volatile int *lock) {
    while (CAS(lock, 0, 1) != 0) {
        // Spin-wait until the lock is acquired
    }
}

In this example, CAS ensures that only one processor can successfully set the lock to 1 (locked) at a time, preventing other processors from entering the critical section. This atomicity applies only to the lock itself, not the shared data inside the critical section.

2. Memory Barriers (Fences)

Memory barriers (also called fences) are used to ensure correct memory ordering by preventing the CPU from reordering memory operations. In multi-core systems, processors often reorder instructions for optimization. For example, a processor might reorder writes and reads to different memory locations to improve performance. However, this can cause inconsistencies when multiple processors are accessing the same data. Without proper memory ordering, one processor might observe stale data even after a lock is released, leading to race conditions.

To enforce proper synchronization, memory barriers ensure that:

• All writes inside a critical section are fully completed before the lock is released.

• All reads after acquiring a lock see the latest version of the shared data.

Memory barriers prevent the CPU from reordering memory operations across the barrier. This ensures that when a lock is released, all cache coherence messages (such as invalidations and write-backs) related to the critical section have fully propagated, and no stale data remains in the system.

Memory Barriers in Action

Consider this scenario:

1. Acquiring the Lock: Processor A uses CAS to acquire the lock. The cache coherence protocol ensures that Processor A’s cache gets exclusive ownership of the lock variable.

2. Modifying Data in the Critical Section: Processor A modifies shared data. During this process, invalidate messages are sent to other caches that might hold copies of the shared data.

3. Releasing the Lock with a Memory Barrier: Before releasing the lock, Processor A issues a memory barrier to ensure that all writes and invalidations complete. The memory barrier ensures that all coherence messages are fully propagated before making the lock available to other processors.

4. Another Processor Acquiring the Lock: Processor B acquires the lock using CAS. After the lock is acquired, the memory barrier ensures that Processor B reads the latest version of the shared data modified by Processor A, with no stale data remaining.

Example: Lock Release with Memory Barrier

// Example of releasing a lock with a memory barrier
void release_lock(volatile int *lock) {
    __memory_barrier();  // Ensure all writes complete before releasing the lock
    *lock = 0;           // Release the lock
}

In this example, the memory barrier ensures that all writes made inside the critical section are fully visible to other processors before the lock is released. This prevents another processor from acquiring the lock and observing stale data.

3. Sequential Consistency and Memory Ordering

The concept of sequential consistency plays a vital role in multi-core systems. Sequential consistency is a memory model in which all processors see memory operations (reads and writes) in the same order. In this model, all memory operations appear to execute in a globally agreed-upon sequence, even if they happen on different cores.

In the absence of sequential consistency, different processors might see memory operations happening in different orders, leading to inconsistent results in shared memory applications. For example, if Processor A writes to a shared variable and Processor B reads the variable shortly after, B might see the old value unless the system ensures proper memory ordering.

Memory barriers help enforce sequential consistency by preventing the CPU from reordering memory operations around critical sections or synchronization points. When a store barrier (write fence) is issued, it ensures that all pending writes are visible to other processors before the next memory operation. Similarly, a load barrier (read fence) ensures that all reads following the barrier observe the latest data.

Example: Ensuring Sequential Consistency

// Example of sequential consistency using memory barriers
void critical_section(volatile int *shared_data, volatile int *lock) {
    acquire_lock(lock);

    // Write to shared data
    *shared_data = 42;

    // Memory barrier ensures that the write to shared_data is visible
    __memory_barrier();

    release_lock(lock);
}

In this example, the memory barrier ensures that the write to shared_data is fully propagated before the lock is released, guaranteeing that any processor acquiring the lock afterward will observe the most up-to-date value of shared_data.

While cache coherence protocols maintain consistency across multiple caches, they do not guarantee atomic propagation of coherence messages like invalidations or write-backs. This means there can be small windows where stale data might still be visible to other processors, creating potential for race conditions. To handle this, multi-core systems rely on a combination of:

• Locks (enforced using atomic operations like CAS and Test-And-Set) to ensure mutual exclusion and atomicity when accessing critical sections.

• Memory barriers to enforce correct memory ordering and ensure that all coherence messages (invalidations, write-backs) are fully processed before proceeding.

• Sequential consistency to provide a globally agreed-upon order of memory operations across all cores.

By using these mechanisms together, multi-core systems ensure that shared data is accessed atomically and consistently, preventing race conditions and maintaining a coherent view of memory across all processors.

Problems

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Problem 1: Cache Coherence Protocols - MSI to MOESIF

Description: In a multi-core system, three processors (A, B, C) each cache a shared memory location. The system uses the MSI cache coherence protocol. Processor A writes to the shared memory location, Processor B reads the same location, and Processor C reads after both A and B have performed their operations.

Question:

1. Describe the state transitions of the shared cache line in processors A, B, and C throughout this operation sequence, assuming all caches start in the Invalid (I) state.

2. If the system were using MOESIF, explain how the Forward (F) state optimizes this situation.

3. Compare the performance of the MOESI and MOESIF protocols in this scenario and highlight which optimizations reduce the overall bus traffic.

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Problem 2: Atomicity of Coherence Messages and Race Conditions

Description: In a system using the MESI coherence protocol, Processor A modifies a shared cache line and sends an invalidation message to Processor B. Before Processor B processes the invalidation, it reads the cache line, potentially reading stale data.

Question:

1. Analyze the race condition caused by the non-atomicity of coherence message propagation. Could Processor B read stale data?

2. Even with a Test-And-Set atomic lock, explain why this atomicity does not guarantee that coherence messages are fully propagated before the lock is released.

3. Propose a solution that involves memory barriers to ensure Processor B reads the most recent data after the lock is released.

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Problem 3: Memory Ordering, Weak Consistency, and Cache Coherence

Description: Consider a multi-core system that operates under a weak consistency model, where memory operations can be reordered between synchronization points, such as memory barriers. Two processors, A and B, are involved in updating two shared variables, x and y, both initialized to 0. The following operations occur:

• Processor A:

    x = 1;
    memory_barrier();   // Ensure the write to x is visible before continuing
    r1 = y;
  

• Processor B:

    y = 1;
    memory_barrier();   // Ensure the write to y is visible before continuing
    r2 = x;
  

In a weakly consistent system, memory operations that occur between the barriers may be reordered by the processor for optimization purposes, meaning that Processor A and Processor B might see the memory updates in a different order than expected. Outside of barriers (either before the first barrier or after the last barrier), memory operations can also be reordered, but operations cannot cross the barriers themselves.

Question:

1. Explain what it means for memory operations to be reordered between barriers in a weak consistency model. How do memory barriers (fences) ensure that critical synchronization points are respected, and why are they important for ensuring consistency in this system?

2. Without the memory barriers, describe a possible sequence where Processor A and Processor B observe stale values of y and x, respectively. Explain how weak consistency allows for this kind of inconsistency.

3. Assume the system uses the MOESI protocol. How does the Owner (O) state help manage consistency in this weak consistency environment, especially when one processor reads a modified value of a shared variable?

4. Discuss the potential consequences of omitting memory barriers in a weakly consistent system. How could stale reads occur even with a coherence protocol in place? Explain how coherence messages, which may still be propagating, could lead to incorrect program behavior.

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Problem 4: Locks, Barriers, and Critical Sections

Description: You are tasked with implementing a shared counter in a multi-core system. The counter is protected by a Test-And-Set lock. The code is:

void increment_counter(volatile int *counter, volatile int *lock) {
    acquire_lock(lock);   // Using Test-And-Set to acquire lock
    *counter = *counter + 1;
    __memory_barrier();   // Ensure all modifications are propagated
    release_lock(lock);   // Release the lock
}

Question:

1. How does Test-And-Set ensure atomicity in lock acquisition? What happens at the cache level during this process?

2. What would happen if the memory barrier was omitted before releasing the lock? Could other processors observe stale values of the counter?

3. Explain how the MOESIF protocol would optimize this situation if multiple processors frequently read the counter after updates, focusing on the Forward (F) state.

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Problem 5: Cache Line Invalidation and Non-Atomic Propagation

Description: In a system using the MESI protocol, Processor A modifies a cache line and sends an invalidation message to Processor B, which holds the line in the Shared (S) state. Processor C attempts to read the line before Processor B processes the invalidation.

Question:

1. Explain how the MESI protocol handles this situation and whether Processor C could read stale data from Processor B's cache.

2. How can memory barriers ensure Processor C reads the updated data?

3. If the system used the MOESI protocol, explain how the Owner (O) state would reduce race conditions by avoiding the need for write-backs to memory.

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Problem 6: Sequential Consistency Violation with Reordering

Description: In a multi-core system, two processors (A and B) are writing to two shared variables x and y. Without memory barriers, Processor A writes to x then reads y, while Processor B writes to y then reads x.

Question:

1. Explain how sequential consistency is violated without memory barriers in this situation.

2. Propose a solution using memory fences to ensure proper ordering and eliminate any inconsistencies in the shared memory reads and writes.

3. How would the MOESIF protocol improve the consistency if one of the processors is reading a frequently shared variable?

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Problem 7: Optimizing Read-Heavy Workloads with Cache Coherence Protocols

Description: You are designing a system where multiple processors frequently read from a shared cache line, but only a few processors occasionally modify it. The system uses the MOESI protocol.

Question:

1. Explain the role of the Owner (O) state in optimizing read-heavy workloads in MOESI.

2. Would switching to the MESIF protocol with the addition of the Forward (F) state further optimize the system? If so, how does the Forward state reduce unnecessary bus traffic in this scenario?

3. Describe a scenario in which MOESIF would outperform both MOESI and MESIF.

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Research Problem 1: Improving Atomicity of Coherence Messages

Description: Despite the sophisticated design of cache coherence protocols, the non-atomic nature of coherence message propagation creates small windows where race conditions or stale data access can still occur.

Research Question:

1. Explore the idea of making coherence message propagation atomic or near-atomic. What would be the theoretical and practical challenges in designing a protocol that minimizes race conditions during invalidations or write-backs? Could we modify existing protocols like MOESIF or invent a new one to handle this issue?

2. Design an experimental protocol or simulation model that reduces the impact of non-atomic coherence message propagation and evaluate its performance in read-heavy and write-heavy scenarios.

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Research Problem 2: Hardware-Assisted Memory Barriers in Coherence Protocols

Description: Memory barriers are currently used by software to enforce correct memory ordering, ensuring that all cache coherence operations are completed before proceeding. However, memory barriers introduce latency and require programmer or compiler intervention.

Research Question:

1. Investigate the potential for hardware-assisted memory barriers that integrate with existing cache coherence protocols (like MOESIF) to minimize latency while ensuring correct memory ordering. Could hardware pre-emptively manage memory consistency without explicit memory barrier instructions?

2. Develop a model where the coherence protocol itself enforces memory ordering, thereby eliminating the need for explicit barriers in software. Assess the potential performance gains and discuss the implications for multi-core architecture designs.

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