Processing-using-DRAM has been proposed for a limited set of basic operations (i.e., logic operations, addition). However, in order to enable full adoption of processing-using-DRAM, it is necessary to provide support for more complex operations. In this paper, we propose SIMDRAM, a flexible general-purpose processing-using-DRAM framework that (1) enables the efficient implementation of complex operations, and (2) provides a flexible mechanism to support the implementation of arbitrary user-defined operations. The SIMDRAM framework comprises three key steps. The first step builds an efficient MAJ/NOT representation of a given desired operation. The second step allocates DRAM rows that are reserved for computation to the operation's input and output operands, and generates the required sequence of DRAM commands to perform the MAJ/NOT implementation of the desired operation in DRAM. The third step uses the SIMDRAM control unit located inside the memory controller to manage the computation of the operation from start to end, by executing the DRAM commands generated in the second step of the framework. We design the hardware and ISA support for SIMDRAM framework to (1) address key system integration challenges, and (2) allow programmers to employ new SIMDRAM operations without hardware changes.We evaluate SIMDRAM for reliability, area overhead, throughput, and energy efficiency using a wide range of operations and seven real-world applications to demonstrate SIMDRAM's generality. Our evaluations using a single DRAM bank show that (1) over 16 operations, SIMDRAM provides 2.0× the throughput and 2.6× the energy efficiency of Ambit, a state-of-the-art processing-using-DRAM mechanism; (2) over seven real-world applications, SIM-DRAM provides 2.5× the performance of Ambit. Using 16 DRAM banks, SIMDRAM provides (1) 88× and 5.8× the throughput, and 257× and 31× the energy efficiency, of a CPU and a high-end GPU, respectively, over 16 operations; (2) 21× and 2.1× the performance of the CPU and GPU, over seven real-world applications. SIMDRAM incurs an area overhead of only 0.2% in a high-end CPU.

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Motivation: Identifying sequence similarity is a fundamental step in genomic analyses, which is typically performed by first matching short subsequences of each genomic sequence, called seeds, and then verifying the similarity between sequences with sufficient number of matching seeds. The length and number of seed matches between sequences directly impact the accuracy and performance of identifying sequence similarity. Existing attempts optimizing seed matches suffer from performing either 1) the costly similarity verification for too many sequence pairs due to finding a large number of exact-matching seeds or 2) costly calculations to find fewer fuzzy (i.e., approximate) seed matches. Our goal is to efficiently find fuzzy seed matches to improve the performance, memory efficiency, and accuracy of identifying sequence similarity. To this end, we introduce BLEND, a fast, memory-efficient, and accurate mechanism to find fuzzy seed matches. BLEND 1) generates hash values for seeds so that similar seeds may have the same hash value, and 2) uses these hash values to efficiently find fuzzy seed matches between sequences. Results: We show the benefits of BLEND when used in two important genomics applications: finding overlapping reads and read mapping. For finding overlapping reads, BLEND enables a 0.9×-22.4× (on average 8.6×) faster and 1.8×-6.9× (on average 5.43×) more memory-efficient implementation than the state-of-the-art tool, Minimap2. We observe that BLEND finds better quality overlaps that lead to more accurate de novo assemblies compared to Minimap2. When mapping high coverage and accurate long reads, BLEND on average provides 1.2× speedup compared to Minimap2.