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Nathan Harris
Nathan Harris

Distributed Memory Multiprocessor Pdf Free


vNUMA, for virtual NUMA, is a virtual machine that presents a cluster as a virtual shared-memory multiprocessor. It is designed to make the computational power of clusters available to legacy applications and operating systems.




Distributed Memory Multiprocessor Pdf Free



A characteristic aspect of vNUMA is that it incorporates distributed shared memory (DSM) inside the hypervisor, in contrast to the more traditional approach of providing it in middleware. We present the design of vNUMA, as well as an implementation on Itanium-based workstations. We discuss in detail the enhancements to standard protocols that were required or enabled when implementing DSM inside a hypervisor, and discuss some of the tradeoffs we encountered. We examine the scalability of vNUMA on a small cluster, and analyse some of the design choices.


This scalable programming model allows the GPU architecture to span a wide market range by simply scaling the number of multiprocessors and memory partitions: from the high-performance enthusiast GeForce GPUs and professional Quadro and Tesla computing products to a variety of inexpensive, mainstream GeForce GPUs (see CUDA-Enabled GPUs for a list of all CUDA-enabled GPUs).


There is a limit to the number of threads per block, since all threads of a block are expected to reside on the same streaming multiprocessor core and must share the limited memory resources of that core. On current GPUs, a thread block may contain up to 1024 threads.


Thread blocks that belong to a cluster have access to the Distributed Shared Memory. Thread blocks in a cluster have the ability to read, write, and perform atomics to any address in the distributed shared memory. Distributed Shared Memory gives an example of performing histograms in distributed shared memory.


Linear memory is typically allocated using cudaMalloc() and freed using cudaFree() and data transfer between host memory and device memory are typically done using cudaMemcpy(). In the vector addition code sample of Kernels, the vectors need to be copied from host memory to device memory:


Thread block clusters introduced in compute capability 9.0 provide the ability for threads in a thread block cluster to access shared memory of all the participating thread blocks in a cluster. This partitioned shared memory is called Distributed Shared Memory, and the corresponding address space is called Distributed shared memory address space. Threads that belong to a thread block cluster, can read, write or perform atomics in the distributed address space, regardless whether the address belongs to the local thread block or a remote thread block. Whether a kernel uses distributed shared memory or not, the shared memory size specifications, static or dynamic is still per thread block. The size of distributed shared memory is just the number of thread blocks per cluster multiplied by the size of shared memory per thread block.


Accessing data in distributed shared memory requires all the thread blocks to exist. A user can guarantee that all thread blocks have started executing using cluster.sync() from Cluster Group API. User also needs to ensure that all the distributed shared memory operations are completed before a thread block exits.


CUDA provides a mechanism to access to distributed shared memory, and applications can benefit from leveraging its capabilities. Lets look at a simple histogram computation and how to optimize it on the GPU using thread block cluster. A standard way of computing histograms is do the computation in the shared memory of each thread block and then perform global memory atomics. A limitation of this approach is the shared memory capacity. Once the histogram bins no longer fit in the shared memory, a user needs to directly compute histograms and hence the atomics in the global memory. With distributed shared memory, CUDA provides an intermediate step, where a depending on the histogram bins size, histogram can be computed in shared memory, distributed shared memory or global memory directly.


The above kernel can be launched at runtime with a cluster size depending on the amount of distributed shared memory required. If histogram is small enough to fit in shared memory of just one block, user can launch kernel with cluster size 1. The code snippet below shows how to launch a cluster kernel dynamically based depending on shared memory requirements.


Memory objects can be imported into CUDA using cudaImportExternalMemory(). An imported memory object can be accessed from within kernels using device pointers mapped onto the memory object via cudaExternalMemoryGetMappedBuffer()or CUDA mipmapped arrays mapped via cudaExternalMemoryGetMappedMipmappedArray(). Depending on the type of memory object, it may be possible for more than one mapping to be setup on a single memory object. The mappings must match the mappings setup in the exporting API. Any mismatched mappings result in undefined behavior. Imported memory objects must be freed using cudaDestroyExternalMemory(). Freeing a memory object does not free any mappings to that object. Therefore, any device pointers mapped onto that object must be explicitly freed using cudaFree() and any CUDA mipmapped arrays mapped onto that object must be explicitly freed using cudaFreeMipmappedArray(). It is illegal to access mappings to an object after it has been destroyed.


A device pointer can be mapped onto an imported memory object as shown below. The offset and size of the mapping must match that specified when creating the mapping using the corresponding Vulkan API. All mapped device pointers must be freed using cudaFree().


A CUDA mipmapped array can be mapped onto an imported memory object as shown below. The offset, dimensions, format and number of mip levels must match that specified when creating the mapping using the corresponding Vulkan API. Additionally, if the mipmapped array is bound as a color target in Vulkan, the flagcudaArrayColorAttachment must be set. All mapped mipmapped arrays must be freed using cudaFreeMipmappedArray(). The following code sample shows how to convert Vulkan parameters into the corresponding CUDA parameters when mapping mipmapped arrays onto imported memory objects.


A device pointer can be mapped onto an imported memory object as shown below. The offset and size of the mapping must match that specified when creating the mapping using the corresponding Direct3D 12 API. All mapped device pointers must be freed using cudaFree().


A CUDA mipmapped array can be mapped onto an imported memory object as shown below. The offset, dimensions, format and number of mip levels must match that specified when creating the mapping using the corresponding Direct3D 12 API. Additionally, if the mipmapped array can be bound as a render target in Direct3D 12, the flag cudaArrayColorAttachment must be set. All mapped mipmapped arrays must be freed using cudaFreeMipmappedArray(). The following code sample shows how to convert Vulkan parameters into the corresponding CUDA parameters when mapping mipmapped arrays onto imported memory objects.


A device pointer can be mapped onto an imported memory object as shown below. The offset and size of the mapping must match that specified when creating the mapping using the corresponding Direct3D 11 API. All mapped device pointers must be freed using cudaFree().


A CUDA mipmapped array can be mapped onto an imported memory object as shown below. The offset, dimensions, format and number of mip levels must match that specified when creating the mapping using the corresponding Direct3D 11 API. Additionally, if the mipmapped array can be bound as a render target in Direct3D 12, the flag cudaArrayColorAttachment must be set. All mapped mipmapped arrays must be freed using cudaFreeMipmappedArray(). The following code sample shows how to convert Direct3D 11 parameters into the corresponding CUDA parameters when mapping mipmapped arrays onto imported memory objects.


A device pointer can be mapped onto an imported memory object as shown below. The offset and size of the mapping can be filled as per the attributes of the allocated NvSciBufObj. All mapped device pointers must be freed using cudaFree().


A CUDA mipmapped array can be mapped onto an imported memory object as shown below. The offset, dimensions and format can be filled as per the attributes of the allocated NvSciBufObj. All mapped mipmapped arrays must be freed using cudaFreeMipmappedArray(). The following code sample shows how to convert NvSciBuf attributes into the corresponding CUDA parameters when mapping mipmapped arrays onto imported memory objects.


We analyze the achievable fault tolerances of shared memory consistency conditions in the form of t-resilience, the ability to withstand up to t node failures. We derive tight bounds for linearizability, sequential consistency, processor consistency, and some weaker memories in totally asynchronous systems, in which failed and slow nodes cannot be distinguished. For linearizability, we show that neither the read nor the write operation can tolerate more failures than a minority of the nodes. For sequential consistency, processor consistency, and related conditions, we show that one operation can be wait-free and the other cannot tolerate more failures than a minority of the nodes. Several weaker conditions can have both operations wait-free. 350c69d7ab


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