And at some point, wafer scale computers have to be integrated and you have the same interconnect issues, but compounded by the density of the wafer itself.īecause multichip module packaging, or MCM, which we often talk about as being a chiplet architecture, has been around for decades – IBM built multichip modules in the System/3081 mainframe 35 years ago that had 133 chips in them and packed the data processing punch of an entire IBM System/370 mainframe in one module from the prior decades ago – we think this will be the way forward for mainstream computing. ![]() And it has to be amendable to workloads that can fit into the SRAM of the circuits as well, or face the same problem of getting wires off the wafer to talk to much slower memories. Wafer scale integration forces component choices to be laid down ahead of time and, unless they include malleable FPGA circuits (which is not a bad idea perhaps), they can’t change. But Dennard scaling stopped in the 2000s and Moore’s Law, as we knew it at least, is using a walker to get around.Īnd so every company that is making compute engines that are being used in the datacenter is confronting the choice between these two approaches. Dennard scaling would still allow clock speeds to rise and we would have 50 GHz chips, and Moore’s Law would allow for transistor costs to keep being cut in half every two years and so the chip size would stay about the same and the performance would just keep going up and up and up. No chip designer likes either of these options, by the way. While we love the idea that you could take all of the circuits embodied in a cluster and put them all on a single silicon wafer – you might be able to get a rack or two of today’s heterogenous HPC and AI nodes devices down to one shiny disk – we think that it is far more likely that system architects will need more flexibility in components than wafer scale integration allows in a lot of cases. ![]() The MI100 delivers up to a 74% generational double precision performance boost for HPC applications.There are two types of packaging that represent the future of computing, and both will have validity in certain domains: Wafer scale integration and multichip module packaging. Up to 184.6 TFLOPs FP16 & 92.3 TFLOPs bFloat16 Peak for Ultra-Fast AI Trainingģ2 GB Ultra-fast HBM2 ECC Memory with upto 1.2 TB/s Memory BandwidthĢ nd Gen Infinity Architecture with up to 340 GB/s of aggregate P2P GPU I/O bandwidthĭelivering up to 11.5 TFLOPs of double precision (FP64) theoretical peak performance, the AMD Instinct™ MI100 accelerator delivers leadership performance for HPC applications and a substantial up-lift in performance over previous gen AMD accelerators. Up to 46.1 TFLOPs FP32 Matrix Peak Performance with All-New Matrix Cores for HPC & AI Workloads World's Fastest HPC GPU with up to 11.5 TFLOPs Peak FP64 Performance Combined with the award winning AMD EPYC™ processors and AMD Infinity Fabric™ technology, MI100-powered systems provide scientists and researchers platforms that propel discoveries today and prepare them for exascale tomorrow.ĭesigned on AMD CDNA Architecture with 120 Compute Units (7,680 cores) MI100 accelerators supported by AMDROCm™, the industry's first open software platform, offer customers an open platform that helps eliminate vendor lock-in, enabling developers to enhance existing GPU codes to run everywhere. Powered by the AMD CDNA architecture, the MI100 accelerators deliver a giant leap in compute and interconnect performance, offering a nearly 3.5x the boost for HPC (FP32 matrix) and a nearly 7x boost for AI (FP16) throughput compared to prior generation AMD accelerators. ![]() ![]() Introducing AMD Instinct™ MI100 Acceleratorįirst Data Center GPU to Surpass 10TF FP64 BarrierĪMD Instinct™ MI100 accelerator is the world's fastest HPC GPU, engineered from the ground up for the new era of computing.
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